# Orbital localization and the role of the Fe and As $4p$ orbitals in BaFe2As2
probed by XANES

A. G. de Figueiredo1 _,_ M. R. Cantarino1 _,_ W. R. da Silva Neto1,2, K. R.
Pakuszewski3, R. Grossi3, D. S. Christovam3∗, J. C. Souza3, M. M. Piva3∗, G.
S. Freitas3, P. G. Pagliuso3, C. Adriano3,__ F. A. Garcia1 1Instituto de
Física, Universidade de São Paulo, São Paulo-SP, 05508-090, Brazil  2Instituto
de Química, Universidade de São Paulo, São Paulo-SP, 05508-090, Brazil  3Inst
Fis Gleb Wataghin, Universidade Estadual de Campinas, Campinas-SP, 13083-859,
Brazil  Current address: Max Planck Institute for Chemical Physics of Solids,
Nöthnitzer Straße 40, 01187 Dresden, Germany

###### Abstract

The polarization dependence of the near edge x-ray absorption spectroscopy
(XANES) is an element specific probe to the real-space distribution of the
density of unoccupied states in solid-state materials. In this paper, we
present Fe and As $K$-edge experiments of Ba(Fe1-x$M_{x}$)2As2 ($M=$ Mn, Co
and $x=0.0$ and $0.08$). The experiments reveal a strong polarization
dependence of the probed XANES spectra, which concerns mainly an increase of
the intensity of electronic transitions when the beam polarization is set out
of the sample’s $ab$ crystallographic plane. The results show that states with
$p_{z}$-orbital character dominate the density of unoccupied states close to
the Fermi level. Partial substitution of Fe by Co is shown to decrease the
intensity anisotropy, suggesting that Co promotes electronic transfer
preferentially to states with $p_{z}$-orbital character. On the other hand, Mn
substitution causes the increase of the spectra $p_{z}$-orbital anisotropy,
which is proposed to take place by means of an enhanced local Fe $3d4p$
mixing, unveiling the role of Fe $4p$ states in the localization of the Fe
$3d$ orbitals. Moreover, by comparing our results to previous experiments, we
identify the relative mixing between Fe and the pnictide $4p_{x,y,z}$ orbitals
as a clear divide between the electronic properties of iron arsenides and
selenides. Our conclusions are supported by multiple-scattering theory
calculations of the XANES spectra and by quantum chemistry calculations of Fe
coordination electronic structure.

##  I Introduction

Doping an electronic correlated phase often results in rich phase diagrams
containing regions dominated by strong electronic correlations and other
regions wherein Fermi liquid behavior is observed. The relatively recently
introduced Fe pnictide (FePns) materials [1] denote a large family of high-
temperature unconventional superconducting materials that seems to lie on the
border between weak and strong electronic correlations [2]. Indeed, the
description of the relevant electronic degrees of freedom in terms of either
localized or itinerant states underlines a major conceptual divide in this
field [3, 4, 5, 6].

The $122$ parent compound BaFe2As2 is a particularly well explored FePn
material. Phases derived from the partial substitution of Fe by Co,
Ba(Fe1-xCox)2As2, is a much debated subject mainly because it leads to a
robust and clean superconducting (SC) ground state [7, 2]. In the case of
Ba(Fe1-xMnx)2As2, however, no SC phase is observed [8]. In principle, partial
substitution at the Fe site by Mn pushes the BaFe2As2 electronic properties to
a Mott region, whereas partial substitution by Co tends to decrease its
electronic correlations. Indeed, the endpoints of the Ba(Fe1-x$M$x)2As2 ($M=$
Mn or Co) materials, BaMn2As2 and BaCo2As2, are characterized as an
antiferromagnetic Mott insulator [9, 10] and a weakly correlated metal [11,
12], respectively, somehow corroborating the proposed substitutional trends.

The BaFe2As2 structure features FeAs layers well spaced by Ba2+ cations, where
Fe is fourfold coordinated by As atoms arranged in a slightly distorted
tetrahedral geometry. This structure breaks the degeneracy of the Fe derived
$3d$ states close to the Fermi level, directly affecting the BaFe2As2 Fermi
surface composition, making the Fe coordination an important parameter to
understanding the $122$ materials electronic properties [2]. In this work, we
focus precisely on the electronic properties of the FeAs coordination complex,
investigating the polarization and composition dependencies of the near edge
(XANES) Fe and As $K$-edges X-ray absorption spectra of Ba(Fe1-x$M$x)2As2
($M=$ Mn or Co, $x=0.0$ and $x=0.08$) single crystals.

Hard X-ray absorption spectroscopy (XAS) experiments could provide a handful
of key information to the field of the FePns materials, including the role of
transition metal substitution on their electronic [13, 14, 15, 16, 17, 18] and
structural properties [19, 20, 21, 22, 23, 24], as well as the degree of
electronic correlations [18, 25, 26, 27] in these systems. Here, we take
advantage of the dipole selection rules dependence on the beam polarization
that makes the XAS spectra strongly angular dependent. Focusing on the XANES
region of the XAS spectra, we thus probe, in real space, the distribution of
the density of unoccupied electronic states in our materials.

For all investigated materials, our results suggest that the density of
unoccupied states at Fermi level has a predominant $p_{z}$ orbital character,
in agreement with the case of the iron arsenide SmFeAsO [28]. Co substitution
is shown to populate preferentially the As $4p_{z}$ orbitals, characterizing a
distinct charge transfer anisotropy. Mn substitution increases the spectra
$p_{z}$ orbital anisotropy suggesting the localization of the Fe $3d$ and A
s$4p$ states, adding yet another piece of evidence that the BaFe2As2 material
can be tuned to a correlated electronic phase [25], but this time not by
doping. From our calculations and experiments, we conclude that the BaFe2As2
electronic structure displays a delicate interplay between the local Fe $3d4p$
mixing and the metal-to-ligand Fe $3d$ As$4p$ mixing. Our findings unveil the
role of the Fe $4p$ states and allow one to make a clear distinction between
the iron arsenides and the iron selenides, for which the unoccupied states
were found to have a predominant Se $4p_{x,y}$ character [29].

Our experiments are analyzed based on two complementary frameworks. We first
present multiple scattering theory calculations (as implemented by the
FEFF$8.4$ code [30, 31]), where we found relative agreement between our
calculations and experiments for the Fe $K$-edge and As $K$-edge.
Nevertheless, FEFF is not able to capture the angular dependence of the probed
spectra. Then, we present multiconfigurational electronic structure
calculations of the orbital states of a single FeAs4 coordination complex, as
implemented by ORCA $5.0$ [32, 33].

##  II Material and Methods

Ba(Fe1-x$M$x)2As2 ($M=$ Mn or Co, $x=0.0$ or $0.08$) single crystals were
synthesized by an In-flux technique as described in Ref. [34]. The Laue
patterns of all samples were obtained to determine the relative orientation of
the $\boldsymbol{a}$ and $\boldsymbol{b}$ crystallographic axis. A composition
($x$) vs. temperature ($T$) phase diagram of Mn and Co substituted samples is
presented in Fig. 1$(a)$ for context. Black squares and blue circles mark the
data from Ref. [35] and red stars mark the data for our samples, as determined
from resistivity measurements [34, 36, 37]. The investigated samples are
indicated by black arrows.

The Fe $K$-edge and As $K$-edge hard X-ray absorption spectroscopy experiments
were performed at the XDS beamline [38] of the Brazilian Synchrotron Light
source (CNPEM-LNLS) at room temperature. Data was acquired by the partial Fe
and As fluorescence. The samples were mounted in a Huber $6+2$-circle
diffractometer with the experimental geometry as explained in Fig. 1$(b)$. The
beam polarization direction is fixed and is aligned to one of the sample
crystallographic axes, which will be hereafter called the $\boldsymbol{a}$
axis. XANES spectra were thus collected for rotations about the in plane
$\boldsymbol{a}$ and $\boldsymbol{b}$ axis and the out of plane
$\boldsymbol{c}$ axis which are, respectively, termed $\theta$, $\phi$ and
$\chi$ rotations (see Fig. 1$(b)$). For all rotations, we adopted an angular
($\alpha$) interval of $0<\alpha<45\text{\textdegree}$. All spectra were
normalized in ATHENA [39].

FEFF calculations [31] were adopted to calculate the XANES spectra. The _ab
initio_ calculations were performed and converged for clusters of up to $282$
atoms for the Fe $K$-edge and $144$ atoms for the As $K$-edge. In both cases,
the Hedin-Lundqvist [40] pseudopotential was adopted to account for the effect
of the local exchange-correlation. Self-consistent calculations were performed
for a cluster radius of $6.5$ Å (Fe $K$-edge) and $8.0$ Å (As $K$-edge).
Quadrupolar transitions and spin orbit coupling effects were considered but
nos sizable effects were observed. Chemical substitution effects were
simulated in the case of the As $K$-edge, by substituting one in ten Fe atoms
by one Mn (Co) atom. The large self-consistent $8$Å radius was adopted to
include all dopants in the self-consistent calculations.

Multiconfigurational electronic calculations of the electronic states of a
FeAs4 tetrahedral like molecule were implemented by ORCA $5.0$ [32, 33]. Ionic
charges were set as $-3$ for each arsenic ion and $+2$ for the metallic
center, resulting in a $S=2$ molecule, consistent with a high spin complex
within tetrahedral like coordination ($D_{2d}$ symmetry). Atomic positions
were obtained from the BaFe2As2 crystallographic data.

To capture the optimized configuration of the electronic states in this $3d$
metal complex, we apply the CAS-SCF method and electronic correlations were
later taken into account by NEVPT-2 calculations. The relativistic adapted
Karlsruhe valence triple-zeta with two sets of polarization functions were
adopted as the basis set within the ZORA approximation [41]. We built a
complete active space (CAS) consisting only of the five $3d$ orbitals and its
six electrons - CAS(6,5) - in the $D_{2d}$ symmetry, and the ligand Field
Parameters were obtained at the end of the calculation. Virtual states, $5$eV
above the Fermi level, were calculated similarly but adopting a CAS(2,12)
[42].

##  III Results and Discussion

Figure 1: $(a)$ Composition ($x$) $vs.$ $T$ phase diagram for the
Ba(Fe1-xMnx)2As2 and Ba(Fe1-xCox)2As2 transition metal substituted iron
arsenides (black squares are data from Ref. [35]). $(b)$ Schematic
representation of the experimental geometry, defining the rotation angles
$\phi$, $\theta$ and $\chi$ as rotations around the $\boldsymbol{a}$,
$\boldsymbol{b}$ and $\boldsymbol{c}$ axis, respectively. $(c)$ Representative
BaFe2As2 Fe $K$-edge normalized XANES spectrum ($\mu(E)$, left axis, full
symbols) and its derivative ($d\mu(E)/dE$, right axis, open symbols). Capital
letters $A$-$E$ mark the transitions and the dashed lines associate the
features in the spectrum derivative to the spectrum. $(d)$ The respective As
$K$-edge data of BaFe2As2, with capital letters $F$-$G$ labeling the
absorption features $(e)-(j)$. Polarization dependence of the Fe $K$-edge and
As $K$-edge XANES spectra of BaFe2As2. The open red circles in the insets of
panels $(f)$ and $(i)$ show the difference spectrum
($\mu(E)_{\phi=45}-\mu(E)_{\phi=0}$) with the dashed lines marking some
representative energy positions.

In Figs.1 $(c)$ and $(d)$ we present representative normalized ($\mu(E)$) Fe
and As $K$edge spectra of BaFe2As2 along with their respective spectra
derivatives. The Fe $K$-edge XANES spectrum presents five absorption features
labeled by capital letters $A-E$. The features are positioned in the mid range
between the maxima and minima of the spectrum derivative. Features $A-C$ are
of electronic nature, whereas features $D$ and $E$ are predominantly due to
scattering processes [13, 15, 14].

The $A$ feature sits at about $E_{\text{F}}$, which is found to be
$E_{\text{F}}\approx 7111.6$ eV. This feature is called the pre-edge and is
understood to stem from a dipolar transition from the Fe $1s$ to Fe$3d$As$4p$
hybrid bands. It comprises a series of energy levels located in a $1-2$ eV
bandwidth about $E_{\text{F}}$ and expresses the properties of the FeAs
tetrahedral coordination complex. Feature $B$ denote transitions to hybrid
states (including Fe$4s$ and As$4p$ states) lying $3$ eV above the
Fe$3d$As$4p$ states. Feature $C$, at about $\approx 7118.6$ eV, is the main
atomic transition of the Fe $K$-edge and stems from Fe $1s\rightarrow 4p$
transitions [43, 44].

The As $K$-edge XANES spectrum in Fig.1 $(d)$ display two main features, which
we called $F$ and $G$. The former identify the main As $1s\rightarrow 4p$
atomic electronic transition which, due to the As coordination, also includes
contributions from Fe $3d$ orbitals. The latter is mainly due to scattering
processes and transitions to excited electronic states $5$ eV above
$E_{\text{F}}$. The $F$ feature sits about $E_{F}\approx 11864.3$ eV.

In Fig.1 $(e)-(g)$ we present the Fe $K$-edge spectra of BaFe2As2 for all
investigated rotations. In its local coordinate frame, rotating the sample is
equivalent to changing the incident beam polarization, which leads to new
selection rules for the dipole transitions. A $\theta$ rotation is thus a
control experiment, which does not change the beam polarization. Indeed, a
direct inspection Fig.1$(e)$ reveal that $\theta$ rotations do not change the
spectra. A $\chi$ rotation probes orbitals with planar components (as the
$p_{x}$ and $p_{y}$ $p$-orbitals ) whereas $\phi$ rotations probe orbitals
with $z$ symmetry, as $p_{z}$ orbitals.

The pre-edge intensities of the observed spectra clearly increase under $\phi$
rotations (Fig.1$(f)$) characterizing the spectra anisotropy. The inset in
Fig.1$(f)$ display the difference spectrum, obtained from making
$\mu(E,\phi=45\text{\textdegree})-\mu(E,\phi=0)$. As it is clear, the
anisotropy is strong in the pre-edge ($A$ feature) but persists in all the
region of the electronic transitions, peaking again close to the main edge
($C$ feature). The red arrow in the figure calls attention to the fact that
the baseline of the spectra coincides in the region below the $E_{\text{F}}$,
excluding a systematic shift of the background signal as a source of the
effect. Our analysis will focus on the electronic transitions and in
particular in the pre-edge transition.

The significant increase in the pre-edge intensity for a $\phi$ rotation shows
that the mixing between Fe$3d$ and As $4p_{z}$ orbitals form states with a
higher density of unoccupied states than the Fe$3d$As$4p_{x,y}$ orbitals. The
observed lack of in-plane anisotropy for a $\chi$ rotation is expected because
the $p_{x}$ and $p_{y}$ orbital symmetry is not reduced in a tetragonal
environment [45]. Indeed, it is to be noted that the Fe derived $3d$ states
are observed via their hybridization with $p$ states and, therefore, the $p$
states symmetries are the relevant properties in discussing the pre-edge
polarization dependence. In addition, the very observation of the XAS
polarization dependence is unexpected since in itinerant electron systems the
ligands are expected to be weak due to screening by conduction electrons.
These results add to the importance of the local electronic properties of
itinerant magnets [46].

In Figs.1 $(h)-(j)$, we show the As $K$-edge spectra data. Here, one can also
observe that $\theta$ and $\chi$ rotations do not change the spectra. Again,
the spectra are clearly anisotropic for $\phi$ rotations, with the absorption
edge becoming more intense. This result provides a direct assessment of the As
$4p_{z}$ relative lower electronic filling and larger anisotropic orbital
character. We also call attention to the red arrow in 1 $(i)$, showing that
the baseline of the spectra coincides in the region below the Fermi level. In
the inset, we show the difference spectrum which evidence the large anisotropy
of both $F$ and $G$ features.

Our next step is to investigate the composition dependence of the above
effects. We start by inspecting the Fe $K$-edge of Mn and Co substituted
samples. Their XANES normalized spectra ($\mu(E)$) are presented in figures
2$(a)-(f)$. The Co substituted sample is a superconductor with $T_{SC}\approx
22$ K whereas the Mn rich sample does not display SC (see Fig. 1$(a)$). The
putative electronic effects of the Co and Mn substitutions would be symmetric
with respect to hole and electron doping making these samples ideal for our
studies. Concerning $\theta$ and $\chi$ rotations, the results are similar to
what was found in the parent compound, whereas the data for $\phi$ rotations
suggest a weak composition dependency of this anisotropy.

The insets in figures 2$(b)$ and $(e)$ present the difference spectra for the
Mn and Co substituted samples, respectively, and show this effect in more
detail. Moreover, the $C$ feature remains markedly anisotropic under $\phi$
rotations, showing that Fe$4p_{z}$ orbitals form bands with a higher
unoccupied density of states than the Fe$4p_{x,y}$ orbitals. This particular
result for the BaFe2As2 parent compound and doped materials is in contrast to
the case of SmFeAsO [28]. In addition, as in previous XANES experiments of
iron arsenides, the $C$ feature is not much affected by Co substitution [13,
16, 14, 17] and is here shown to be unaffected by Mn substitution as well.

Figure 2: Fe $K$-edge XANES spectra of $(a)-(c)$ Ba(Fe1.92Mn0.08)2As2 and
$(d)-(f)$ Ba(Fe1.92Co0.08)2As2 as a function of the polarization (see figures’
top). In all cases, the normalized intensities ($\mu(E))$ are presented. The
open red circles in the insets of panels $(b)$ and $(e)$ show the difference
spectrum ($\mu(E)_{\phi=45}-\mu(E)_{\phi=0}$) with the dashed lines marking
some representative energy positions.

The composition effect is better observed by our analysis in figures
3$(a)-(c)$. To quantify the intensities anisotropy as a function of $\phi$, we
fit the Fe $K$-edge spectra by a Lorentzian (the $A_{1}$ peaks) and Gaussian
(the $A_{2}$, $B$, $C$, $D$ and $E$ peaks) lineshapes plus a Fermi-Dirac
function as in figure 3$(a)$. The two peaks in the pre-edge region are clearly
present in high-resolution experiments [17] and are suggested by the analysis
of our spectra derivatives. Indeed, adopting two peaks is instrumental to
extract a consistent fitting analysis. The resonance intensities
$I_{\text{peak}}$ are estimated from the peak areas, as in the shades of
figure 3$(a)$.

The $A_{1}$ feature sits just at $E_{F}$ whereas the $B$ feature lies $\approx
3$ eV above it. We thus compare the chemical substitution electronic effects
in these two distinct situations by tracking the anisotropy behavior of the
$A_{1}$ and $B$ features. In figures 3$(b)-(c)$, respectively, we plot the
$I_{A_{1}}$ and $I_{B}$ normalized intensities as functions of $\phi$ for the
compositions as indicated. Each data set is normalized as
$I_{\text{peak}}(\phi)/I_{\text{peak}}(\phi=0)$.

The $A_{1}$ peak of the Mn substituted sample is distinctly more anisotropic,
but the composition effect is not present in the case of the $B$ feature.
Since the latter lies $\approx 3$ eV above $E_{\text{F}}$, it would be hardly
affected by chemical substitution, as observed. A naive interpretation about
the effect of Mn substitution is that Mn “doping” fills the Fe$3d$As$4p$
hybrid bands with holes, increasing the amount of unoccupied states. At the
present “doping” level, however, Mn impurities do not act as charge dopants to
BaFe2As2 [47, 48] and we shall return to this discussion later by proposing a
distinct mechanism for the effects of chemical substitution.

In many instances [30, 49, 31], FEFF calculations provide a first approach to
the interpretation of the XANES spectra. In figure 3$(d)$ we show polarization
dependent FEFF calculations of the BaFe2As2 Fe $K$\- edge spectra, and their
derivatives, compared to experimental data. The calculations reproduce well
the $B-E$ features position but their polarization dependencies are not fully
reproduced. Moreover, the $A$ feature is missed completely, reflecting that
FEFF calculations do not capture in full the properties of bound states [49,
30, 31].

If core-hole effects can be considered weak, the nature of the observed
transitions can be associated with the element (site) and orbital projected
local density of states (LDOS). In figure 3$(e)$, we present the LDOS obtained
from FEFF calculations, focusing on the Fe and As derived states. High
densities of states are predicted at the positions of the $A$, $B$ and $C$
features. In particular, the $B$ and $C$ features correlate, respectively, to
the LDOS due to the Fe $4s$ and As and Fe $4p$ states. As anticipated in our
discussion, the $A$ feature can be associated with a high LDOS derived from Fe
$3d$ and As $4p$ states. In addition, our calculations also predict a high
density of Fe $4p$ states about this same region, inviting an investigation
into the role of the local Fe$3d4p$ hybridization, which we shall discuss
based on quantum chemistry calculations. The inset of figure 3$(e)$, compares
the FEFF calculated spectra with and without core-hole effects and the close
similarity between the calculations suggests that our discussion is adequate
in a first approximation.

Figure 3: $(a)$ Fe $K$\- edge XANES spectra of BaFe2As2 ($\phi=0$ and
$\phi=45\text{\textdegree})$ and their respective phenomenological fittings.
As shown, two peaks, termed $A_{1}$ and $A_{2}$, are included to describe the
pre-edge $A$ feature. The shaded regions below the curves are the peak areas
which are adopted as the resonance intensities. The obtained intensities are
presented in $(b)$ and $(c)$ for, respectively, the $A_{1}$ pre-edge peak and
the $B$ edge peak as a function of $\phi$ and composition. In $(d)$ we present
polarization-dependent FEFF calculations of the Fe $K$-edge spectra (left
axis) and its derivative (right axis) compared the respective experimental
data. A shift of $-1.3$ eV and a broadening of $1$ eV were considered in the
calculations. In panel $(e)$, we show the FEFF calculated site and orbital
projected LDOS of BaFe2As2. Features $A$, $B$ and $C$ are presented for
comparison to the results. The inset of the figure compares the FEFF
calculated spectra ($\phi=45\text{\textdegree}$) with and without core-hole
effects.

We now turn to the As $K$-edge experiments of other compositions. In figures
4$(a)-(f)$, the normalized intensities ($\mu(E)$) of the As $K$-edge XANES
spectra of the doped samples are presented. Again, the measured spectra are
isotropic under $\theta$ and $\chi$ rotations. In the case of $\phi$
rotations, however, both Ba(Fe1.92Mn0.08)2As2 and Ba(Fe1.92Co0.08)2As2 spectra
are polarization-dependent but this time the composition effect is
straightforwardly observed by direct inspection of figures 4$(b)$ and 4$(e)$.
The insets of the same figures present the respective difference spectrum
($\mu(E)_{\phi=45}-\mu(E)_{\phi=0}$). The insets are on the same scale, making
it clear that the XANES anisotropy is larger for the Mn rich material.
Moreover, it is also clear that the edge anisotropy of the Co substituted
sample decreases when compared to the case of the parent compound, whereas it
increases for the Mn containing sample. Since the As $K$-edge is a direct
probe to the properties of the As $4p_{x,y,z}$ orbitals sitting at about
$E_{\text{F}}$, the distinction between the localization and occupation of the
As $4p_{z}$ orbitals should be more evident, as observed.

The anisotropy of the post edge feature at about $11870$ eV, which is about
$5$ eV above the Fermi level, is also affected by composition, being less
intense for the Co substituted sample. Since this feature is well above the
Fermi level, the composition effect is most likely due to the direct effect of
the impurity scattering potential [50, 51] and we conclude that Mn impurities
act as stronger scattering centers than Co.

Figure 4: As $K$-edge XANES spectra of $(a)-(c)$ Ba(Fe1.92Mn0.08)2As2 and
$(d)-(f)$ Ba(Fe1.92Co0.08)2As2 as a function of the polarization (see figures’
top). In all cases, the normalized intensities ($\mu(E))$ are presented. The
open red circles in the insets of panels $(b)$ and $(e)$ show the difference
spectrum ($\mu(E)_{\phi=45}-\mu(E)_{\phi=0}$) with the dashed lines marking
some representative energy positions.

To estimate the composition effect in the edge intensity anisotropy as a
function of $\phi$ [$I(\phi)$], we adopt again the integrated areas of the
edge features as the approximation to $I(\phi)$. This time, we undertake a
direct approach numerically integrating the experimental data, as exemplified
in figure 5$(a)$. Indeed, results from a lineshape analysis of the As $K$-edge
are too much dependent on the choice of parameters and we found that a
numerical integration suffices to properly capture our results. For each
$\phi$, $I(\phi)$ is obtained and then the data is normalized as
$I(\phi)/I(\phi=0)$. Results for all samples are shown in figure 5$(b)$. The
error bars are estimated by small variations of the integration region. In
comparison to the parent compound, the effect of Co substitution is clear,
whereas that of Mn is weak but significant.

In Ref. [16], Co substitution is suggested to populate the As derived
orbitals. Here, we show that the electrons derived from Co substitution go
preferentially to the unoccupied As$4p_{z}$ states, characterizing a distinct
charge transfer anisotropy. By causing an electronic transfer to orbitals
along the $c$-axis, Co substitution unbalances the electron and holes
contributions to the transport along this direction, increasing the incoherent
scattering in this direction. We thus support the interpretation given in Ref.
[52] to the observed BaFe2As2 inter plane resistivity anisotropy and its
increase with Co substitution [53, 52]. Moreover, our results provide a real
space picture of the evolving $3$D character of the BaFe2As2 electronic
structure as previously probed by angle-resolved photoemission spectroscopy
(ARPES) [54].

On the other hand, no change in electronic filling can be invoked as a
mechanism to the anisotropy increase caused by Mn substitution. As we will
discuss, this effect is connected to the localization of the Fe-derived
electronic states. Indeed, isoelectroctronic substitutions, as observed by
resonant inelastic x-ray scattering experiments of Mn- and P-substituted
BaFe2As2 [36, 55], may increase electronic correlations.

We performed polarization-dependent FEFF calculations, aiming at describing
two effects: the spectra anisotropy and the composition dependence of this
effect. In figure 5$(c)$, we compare the experimental and calculated As
$K$-edges of BaFe2As2 for $\phi=0$ and $\phi=45\text{°}$ and their respective
derivatives. The overall spectral shape and anisotropy are well reproduced but
there is a lack of detail in the effect of the anisotropy. In the
calculations, the edge peaks nearly coincide whereas in the experiments the
edge intensity of the $\phi=45\text{°}$ spectra sits above the $\phi=0$
spectra for all compositions.

To simulate the effects of chemical substitution, we performed FEFF
calculations replacing one in ten Fe atoms with a dopant (either Mn or Co).
This is equivalent to a $x=0.1$ composition, which is close to our samples for
which $x=0.08$. Three dopant distributions were calculated and then averaged
out. In figure 5$(d)$, we compare the difference spectrum
($\mu(E)_{\phi=45}-\mu(E)_{\phi=0}$) obtained from the BaFe2As2 data (open red
circles) and from FEFF calculations of the parent compound and substituted
samples (thick lines). As can be observed, the edge polarization dependence is
partially reproduced but only a small composition effect is observed in the
FEFF calculations.

In figure 5$(e)$, we show the site and orbital projected LDOS obtained from
FEFF calculations. The $F$ feature position is marked for comparison. It shows
that the main edge is dominated by As $4p$ states, as expected. Moreover, the
high density of Fe $3d$ states about the edge position allows the formation of
Fe$3d$As$4p$ hybrid bands making the As $K$-edge transition sensitive to this
mixing. The LDOS of the Fe $4p$ derived states also peaks about the $F$, as
also calculated in the case of the Fe $K$-edge, further suggesting its
relevance to the electronic states about the Fermi energy. Here, due to the As
$K$-edge higher energy, core-hole effects are likely less relevant than for
the Fe $K$-edge, (see inset of figure 3$(e)$), validating the present
discussion in a first approximation.

Figure 5: $(a)$ Normalized intensities ($\mu(E)$) of the As $K$-edge XANES
spectra of Ba(Fe1.92Mn0.08)2As2 for $\phi=0$ and $\phi=45\text{\textdegree}$.
In each case, the panel shows the spectrum region taken into consideration to
perform the numerical integration that represents the $F$ feature intensity
($I_{F}(\phi)$). $(b)$ The polarization dependence of the $F$ peak intensity
as a function of $\phi$, for all compositions. In $(c)$ we present
polarization-dependent FEFF calculations of the As $K$-edge spectra (left
axis) and its derivative (right axis) compared the respective experimental
data. A shift of $1$ eV and a broadening of $1.2$ eV were considered in the
calculations. In panel $(d)$, we present the difference spectrum of the
BaFe2As2 data (open red circles) compared to FEFF calculations of
Ba(Fe1-x$M_{x}$)2As2 , with $M=$ Mn or Co and $x=0.1$ (see main text). $(e)$
FEFF calculated site and orbital projected LDOS of BaFe2As2 focusing on the Fe
and As derived electronic states.

Whereas our FEFF calculations for the Fe and As K edges are able to support
our discussion of the experimental results, it is clear that some aspects of
the physics of our system are not captured. This is nothing but an expression
of the still unresolved character of the electronic states of the FePns
materials, that lay in the border between strong and weak electronic
correlations. We thus resort to quantum chemistry calculations of the
electronic properties of a single FeAs4 distorted tetrahedron, within a
multiconfigurational calculation of the orbital states [32, 33, 42, 41].

Being XANES a local probe to electronic structure, it is reasonable to assume
that the electronic structure of a single FeAs4 distorted tetrahedron can be
connected to the pre-edge feature which, insofar as the Fe $K$-edge is
concerned, is the main focus of our paper. Our results are presented in figure
6 and in table 1. In doing that, we are assuming that the XANES transitions in
our materials are mainly due to local processes, which excludes the metal-
metal charge transfer that is observed in certain transition metal complexes
[56, 57]. One can justify this assumption based on the As chemistry, which
tends to form complexes with a low bridging character, with the As $4p$ states
well-localized on the As sites. This orbital localization would weaken the Fe-
Fe charge transfer that relies on the mediation of the ligand orbitals.

The multiconfigurational calculations results show that the first set of
partially occupied states are indeed ligand field (or crystal field) levels
formed by hybrid orbitals from Fe$3d$ and As$4p$ states. In our discussion, we
name the $\mathcal{j}a_{1}^{*}\mathcal{i}$, $\mathcal{j}b_{2}^{*}\mathcal{i}$,
$\mathcal{j}e^{*}\mathcal{i}$ and $\mathcal{j}b_{1}^{*}\mathcal{i}$ molecular
orbitals by their Fe $3d$ main character, respectively, $d_{z^{2}}$,$d_{xy}$,
$d_{xz}/d_{yz}$ and $d_{x^{2}-y^{2}}$. The calculated ligand field splitting
of only $\approx 0.3$ eV is in qualitative agreement with previous
calculations [58].

From table 1 and from the isosurface plot of the molecular orbital
wavefunctions in figure 6, one can observe that all $d_{xy}$, $d_{xz}/d_{yz}$
and $d_{x^{2}-y^{2}}$ hybridize with the As $4p_{x,y,z}$ orbitals. The
$d_{z^{2}}$ and $d_{xy}$ orbitals appear in our calculations as nearly
degenerate states and are indicated to be virtually double occupied.
Electronic transitions are thus dominated by the $d_{xz}/d_{yz}$ and
$d_{x^{2}-y^{2}}$ hybrid orbitals.

All the $d_{xz}/d_{yz}$ and $d_{x^{2}-y^{2}}$ display contributions with
$p_{z}$ orbital character and therefore contribute to the increase of the
spectra intensity for $\phi$ rotations. It should be noted that even if the
$d_{xy}$ orbital were not double occupied, it would not contribute to this,
since they present no mixing with $p_{z}$ orbitals. The calculated
hybridization pattern of the ligand field orbitals is imposed by the specific
properties of the FeAs4 complex. Indeed, the $D_{2d}$ symmetry would allow the
mixing of $p_{z}$ orbitals into the $d_{xy}$ orbital, whereas it would prevent
any $p_{z}$ orbital mixing with $d_{x^{2}-y^{2}}$.

The most prominent feature to be observed from our calculations is the
contribution from the Fe$4p$ states to ligand field orbitals, forming Fe$3d4p$
hybrid states. This local $pd$ hybridization is acquired by the Fe$3d$ states
as a formal way of reducing their antibonding character, which is implied by
the $\mathrm{As^{-3}}$ $\pi$-donation. This effect is a mechanism for the
localization of the Fe$3d$ states, as observed in other coordination complexes
[59]. As a consequence, the pre-edge peak in the Fe $K$-edge can be attributed
to Fe$3d4p$ hybridization in Fe complexes [60] and our findings propose that
we should reconsider the nature of the pre-edge transition of the Fe $K$-edge
of the FePns materials. Indeed, so far in our discussion, as well as in
previous works [25, 18, 26, 27, 28], the contribution of the Fe$4p$ states to
the pre-edge was overlooked. We shall argue that this local hybridization is
key to understand the effects of Mn substitution.

First, we expect that via the Fe$3d$As$4p$ mixing, the Fe $K$-edge results
should mirror that of the As $K$-edge, as observed. This expectation, however,
does not take into account that the pre-edge intensity may be dominated by
transitions to the Fe$3d4p$ states. This is likely the case of FeSe materials
[29], for which the Se $K$-edge clearly indicates that the Se$4p_{x,y}$ planar
orbitals dominate the density of unoccupied states, whereas the contrary is
concluded from the Fe $K$-edge.

Mn substitution may weak the Fe$3d$As$4p$ mixing, making the Fe orbitals more
localized through the mechanisms above explained. In turn, this would make the
Fe$3d4p_{z}$ mixing stronger, increasing the observed polarization dependence.
In the same direction, the As $4p$ will also become more localized, rendering
the As $K$ edge spectra more anisotropic. Both effects are observed in figures
3$(b)$ and 5$(b)$ and we propose that this is the mechanism related to Mn
substitution in BaFe2As2: Mn impurities localize the Fe$3d$ states by changing
the Fe$3d$As$4p$ mixing. In turn, it shows that hole doping is not the only
active mechanism pushing BaFe2As2 to a more correlated Mott phase.

Figure 6: Left panel: the energetic ordering of fully occupied, ligand field and virtual orbitals as obtained from our calculations. Right panel: ligand field molecular orbitals and their ground state configuration based on CAS(6,5)/NEVPT-2/Def2-TZVPP approach [41]. Isosurfaces values are set to $0.006$. Our results indicate a ligand field splitting of $0.308$ eV. Ligand Orbitals are plotted via the Gabedit program [61] Table 1: Metal and ligand orbital composition of the FeAs4 ligand field molecular orbitals. Numbers are given in percentage of normalized wavefunctions. The Final orbital composition analysis was performed adopting the Ros-Schuit partition method via the Multifwn program [62].  | Iron orbitals (MO symmetry) |  $3d_{z^{2}}$ $(a_{1}^{*})$ |  $3d_{xy}$ $(b_{2}^{*})$ |  $3d_{xz},3d_{yz}$ $(e^{*})$ |  $3d_{x^{2}-y^{2}}$ $(b_{1}^{*})$
---|---|---|---|---|---
| Ligand Field Relative energies / eV | 0.000 | 0.002 | 0.260 | 0.308
Fe orbitals | $4p_{z}$ |  |  | 4.48 | 2.25
$4p_{y}$ and $4p_{x}$ | 5.80 |  | 10.32 | 5.80
As Orbitals | $4p_{z}$ | 2.75 |  | 2.00 | 2.38
$4p_{y}$ and $4p_{x}$ | 0.89 | 4.15 | 7.79 | 5.80

##  IV Summary and Conclusions

We have investigated the polarization dependence of the Fe and As $K$-edges
XANES spectra of BaFe2As2 and chemically substituted Mn and Co materials of
this parent compound. In the case of the Fe $K$-edge, we focused our analysis
on the transitions allowed by Fe$3d$As$4p$ hybrid orbitals that spam the pre-
edge structure of the Fe $K$-edge spectra. In the case of the As $K$-edge, we
focused on the edge transitions, probing the As $4p_{x,y,z}$ orbitals.

The polarization dependence indicates that Co substitution populates
preferentially the hybrid bands with unoccupied states along the $c$ axis
($p_{z}$ orbitals), as concluded from the strong reduction of the As $K$-edge
anisotropy. This is a distinct anisotropic charge transfer effect, which may
be connected to the transport properties and ARPES experiments of Co doped
BaFe2As2 [52, 53, 54].

Mn substitution, whereas not changing the material electronic filling,
increases the anisotropy of the probed electronic states. We attributed this
finding to a delicate interplay between the local Fe$3d4p$ and the metal-
ligand Fe$3d$As$4p$ mixings, with Mn substitution favoring the Fe$3d$
localization by hindering the Fe$3d$As$4p$ mixing.

In all cases, the XANES polarization dependence revealed a higher density of
unoccupied state for orbitals with $p_{z}$ character, with the results from
the Fe $K$-edge mirroring those of the pnictide $K$-edge. Our quantum
chemistry calculations show this is not the only result that could be
expected, since the local Fe$3d4p$ hybridization also contributes to the pre-
edge transitions and may dominate its polarization dependence. This is likely
the case of FeSe materials [29]. One can thus state a clear distinct behavior
of the electronic states of iron arsenides and selenides, with the former
presenting a stronger Fe$3d$As$4p$ hybridization, which favors the occupation
of orbitals with planar geometry, whereas orbitals along the $c$-axis remain
unoccupied.

Overall, our findings suggest that the interplay between the local Fe$3d4p$
and the metal-ligand Fe$3d$As(Se)$4p$ mixings is a common thread of the FePns
electronic structure, unveiling the key role played by Fe $4p$ states.

##  V Acknowledgments

The authors acknowledge CNPEM-LNLS for the concession of beamtime at the XDS
beamline (proposals Nos. 20180194 and 20190123). The XDS beamline staff is
acknowledged for their assistance during the experiments. The Fundação de
Amparo à Pesquisa do Estado de São Paulo (FAPESP) financial support is
acknowledged by M.R.C. (#2019/05150-7 and #2020/13701-0) W.R.S.N.
(#2019/23879-4), D.S.C. (#2019/04196-3), J.C.S. (#2018/11364-7 and
#2020/12283-0), M.M.P. (#2015/15665-3), P.G.P. and C.A. (#2017/10581-1) and
F.A.G. (#2019/25665-1). P.G.P. and C.A. acknowledge the financial support from
CNPq: CNPq # 304496/2017-0 and CNPq #310373/2019-0.

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# M2MeT: THE ICASSP 2022 MULTI-CHANNEL MULTI-PARTY MEETING TRANSCRIPTION
CHALLENGE

###### Abstract

Recent development of speech signal processing, such as speech recognition,
speaker diarization, etc., has inspired numerous applications of speech
technologies. The meeting scenario is one of the most valuable and, at the
same time, most challenging scenarios for speech technologies. Speaker
diarization and multi-speaker automatic speech recognition in meeting
scenarios have attracted increasing attention. However, the lack of large
public real meeting data has been a major obstacle for advancement of the
field. Therefore, we release the _AliMeeting_ corpus, which consists of 120
hours of real recorded Mandarin meeting data, including far-field data
collected by 8-channel microphone array as well as near-field data collected
by each participants’ headset microphone. Moreover, we will launch the Multi-
channel Multi-party Meeting Transcription Challenge (M2MeT), as an ICASSP2022
Signal Processing Grand Challenge. The challenge consists of two tracks,
namely speaker diarization and multi-speaker ASR. In this paper we provide a
detailed introduction of the dateset, rules, evaluation methods and baseline
systems, aiming to further promote reproducible research in this field.

Index Terms—  AliMeeting, meeting transcription, automatic speech recognition,
speak diarization, meeting scenario

##  1 Introduction

Meeting transcription is the task to address the “who speaks what at when”
problem in real-world multi-speaker meetings. Such scenarios are considered as
one of the most challenging tasks in speech processing due to free speaking
styles and complex acoustic conditions such as overlapping speech, unknown
number of speakers, far-field signals in large conference rooms, noise and
reverberation etc. As a result, tackling the problem requires a well designed
speech system with functionalities from multiple related speech processing
components including but not limited to front-end signal processing, speaker
identification, speaker diarization and automatic speech recognition (ASR),
etc.

The lack of sizable labeled meeting data is one of the main bottlenecks to the
current development of advanced meeting transcription system. Design of such a
corpus desires both complex room and recording setup as well as time-consuming
human transcriptions. Since meeting transcription involves numerous related
processing components, more information have to be carefully collected and
labelled, such as speaker identity, speech context, onset/offset time, etc.
All these information require precise and accurate annotations, which is
expensive and time-consuming. Although several relevant datasets have been
released [1, 2, 3, 4, 5], as discussed in [6], most of them suffer from
various limitations, ranging from corpus setup such as corpus size, number of
speakers, variety of spatial locations relative to the microphone arrays,
collection condition, etc., to corpus content such as recording quality,
accented speech, speaking style, etc. Moreover, almost all public available
meeting corpora are collected in English, and the differences among different
languages limit the development of Mandarin meeting transcription. A publicly
available large real-recorded Mandarin speech dataset in meeting scenario is
in great necessity. A common evaluation platform to promote the research in
this field is also necessary.

In this paper, we release AliMeeting, a sizeable Mandarin meeting corpus,
containing 120 hours real meeting data recorded by 8-channel directional
microphone array and headset microphone. To the best of our knowledge,
AliMeeting and Aishell-4 are currently the only publicly available meeting
datasets in Mandarin. The recently released Aishell-4 [6] covers a variety of
aspects in real-world meetings, including diverse recording conditions,
various number of meeting participants, various overlap ratios and noise
types, and meets the modern requirement of accurate labeling. On the basis of
these advantages, AliMeeting increases the number of speakers and meeting
venues to enhance generalization, while particularly adding the discussion
scene with a high speaker overlap ratio in the meeting. High-quality
transcriptions on multiple aspects are provided for each meeting, allowing the
researcher to explore different aspects of meeting processing. Furthermore,
the AliMeeting corpus provides not only far-field data collected by 8-channel
microphone array but also near-field data collected by the headset microphone
of each participant which may benefit data augmentation. Noticeably, our
AliMeeting dataset will be presented along with Aishell-4 [6] as M2MeT
challenge training data for researchers in academia and industry to better
facilitate researches on meeting transcription .

Meeting transcription usually contains three major subtasks, namely front-end
processing, speaker diarization and speech recognition. Firstly, speech
enhancement [7] and separation [8], also known as front-end processing, aims
to handle the complex acoustic environment including noisy and speech
overlapped scenarios. Thanks to the rapid development of deep learning in
recent years, speech enhancement and separation have received remarkable
progress and consistent improvements on public datasets, e.g., WSJ0-2mix [9],
VoiceBank-DEMAND [10] and REVERB Challenge [11]. However, in real meeting
scenarios, there are still many problems to be solved, such as noise,
reverberation and signal attenuation, overlapping mismatch between real
scenarios and most hypotheses in current researches, etc. Secondly, speaker
diarization is the task to label the audio with classes corresponding to
speaker identity. Traditional speaker diarization usually contains an
embedding extraction, and a clustering step, where the input audio stream is
firstly converted into speaker-specific representation [12, 13], followed by a
clustering process that aggregates the regions of each speaker into separated
clusters. Thanks to the powerful modeling capabilities of deep learning, the
extraction of speaker embedding using neural networks is the most widely
adopted method in recent years, such as d-vectors [12] and i-vectors [13].
Recently, researchers proposed end-to-end neural diarization (EEND) [14],
which replaces the individual sub-modules in traditional speaker diarization
systems mentioned above with one neural network and gets promising results.
Nevertheless, the problem of generalization of mismatch between real data and
synthetic data, the counting problem of variable number of speakers, and the
poor performance of overlapping speech recognition are still huge challenges
in real meeting scenarios. Finally, the accuracy and efficiency of ASR have an
important impact on the user experience in the meeting transcription task. A
drastic improvement in accuracy and robustness has been brought to modern
single speaker ASR system [15, 16], and several works show promising results
in multi-speaker ASR [17, 18] by employing synthetic datasets for
experimentation. However, the problem of unknown number of speakers, variable
overlap rate, accurate speaker identity assignment for utterances are still
considered unsolved, especially for real-world applications. As stated in
[17], ASR for multi-speaker speech remains to be a very difficult problem
especially when speech utterances from multiple speakers significantly overlap
in monaural recordings.

In order to inspire research on advanced meeting rich transcription, we
propose the Multi-channel Multi-party Meeting Transcription Challenge (M2MeT)
based on the new AliMeeting corpus introduced above as well as the recently-
released Aishell-4 corpus [6]. We particularly set up two main challenge
tracks of speaker diarization and multi-speaker ASR. Meanwhile, we also set up
two sub-track for both tracks: the first sub-track limits the usage of data
while the second sub-track allows the participants to use extra constrained
data. Rules and baseline systems are also released along with the corpus to
further facilitate the research in meeting transcription.

Fig. 1:  An example of recording venue of the Train set and the topology of
microphone array.

##  2 The AliMeeting Corpus

AliMeeting contains 118.75 hours of speech data in total. The dataset is
divided into 104.75 hours for training (Train), 4 hours for evaluation (Eval)
and 10 hours as test set (Test) for challenging scoring and ranking.
Specifically, the Train and Eval sets contain 212 and 8 sessions,
respectively. Each session consists of a 15 to 30-minute discussion by a group
of participants. The total number of participants in Train and Eval sets is
456 and 25, with balanced gender coverage. The Train and Eval sets will be
released to the participants at the beginning of the challenge, while the Test
set audio will be released at the final challenge scoring stage.

The dataset is collected in 13 meeting venues divided into three types: small,
medium, and large rooms, whose sizes range from 8 to 55 m2. Different rooms
give us a variety of acoustic properties and layouts. The detailed parameters
of each meeting venue will be released together with the Train data. The type
of wall material of the meeting venues covers cement, glass, etc. Other
furnishings in meeting venues include sofa, TV, blackboard, fan, air
conditioner, plants, etc. During recording, the participants of the meeting
sit around the microphone array which is placed on the table and conduct a
natural conversation. The microphone-speaker distance ranges from 0.3 to 5.0
m. All participants are native Chinese speakers speaking Mandarin without
strong accents. During the meeting, various kinds of indoor noise including
but not limited to clicking, keyboard, door opening/closing, fan, bubble
noise, etc., are made naturally. For both Train and Eval sets, the
participants are required to remain in the same position during recording.
There is no speaker overlap between the Train and Eval set. An example of the
recording venue from the Train set is shown in Fig. 1.

The number of participants within one meeting session ranges from 2 to 4. To
ensure the coverage of different overlap ratios, we select various meeting
topics during recording, including medical treatment, education, business,
organization management, industrial production and other daily routine
meetings. The average speech overlap ratio of Train and Eval sets are 42.27 %
and 34.76 %, respectively. More details of AliMeeting are shown in Table 1. A
detailed overlap ratio distribution of meeting sessions with different numbers
of speakers in the Train and Eval set is shown in Table 2.

Table 1: Details of AliMeeting Tran and Eval data. | Train | Eval
---|---|---
Duration (h) | 104.75 | 4.00
#Session | 212 | 8
#Room | 12 | 5
#Participant | 456 | 25
#Male | 246 | 12
#Female | 210 | 13
Overlap Ratio (Avg.) | 42.27% | 34.76%
Table 2: Overlap rate distribution of meeting sessions with the different number of speakers. The number followed overlap ratio means the number of sessions with corresponding meeting speaker number. Speaker number | Train | Eval
---|---|---
2,3,4 | 42.27% / 212 | 34.76% / 8
2 | 14.65% / 71 | 17.28% / 3
3 | 32.43% / 15 | 27.21% / 1
4 | 59.00% / 126 | 49.75% / 4

We also record the near-field signal of each participant using a headset
microphone and ensure that only the participant’s own speech is recorded and
transcribed. It is worth noting that the far-field audio recorded by the
microphone array and the near-field audio recorded by the headset microphone
will be synchronized to a common timeline range. All scripts of the speech
data are prepared in TextGrid format for each session, which contains the
information of the session duration, speaker information (number of speaker,
speaker-id, gender, etc.), the total number of segments of each speaker, the
timestamp and transcription of each segment, etc. We also provide unprocessed
raw text in which non-speech events are transcribed as well, such as pauses,
noise, overlap, etc.

##  3 TRACK SETTING AND EVALUATION

###  3.1 Speaker Diarization (Track 1):

Speaker diarization, also known as speaker segmentation clustering, addresses
the “who speak at when” question by logging speaker-specific speech events on
multi-speaker audio data. The constrained data set by the organizer includes
AliMeeting, Aishell-4 [6], and CN-Celeb [19]. The Train and Eval data of
AliMeeting provides not only the audio of the far-field meeting scene recorded
by the microphone array with multiple speakers talking, but also the near-
field audio recorded by each participant’s headset microphone. The Test set,
which includes 10 hours of meeting data, will be released shortly (according
to the timeline) for challenge scoring and ranking. Meanwhile, the organizers
will only provide the far-field audio in the Test set recorded by 8-channel
microphone array and the corresponding sentence segmentation timestamp. Note
that the organizers will not provide the headset near-field audio and the
transcriptions. Participants need to determine the speaker at each time point,
and an RTTM file needs to be provided for each session.

The accuracy of speaker diarization system in this track is measured by
Diarization Error Rate (DER) [20] where DER is calculated as: the summed time
of three different errors of speaker confusion (SC), false alarm (FA) and
missed detection (MD) divided by the total duration time, as shown in

$\text{DER}=\frac{\mathcal{T}_{\text{SC}}+\mathcal{T}_{\text{FA}}+\mathcal{T}_{\text{MD}}}{\mathcal{T}_{\text{Total}}}\times
100\%,\vspace{-0.2cm}$ (1)

where $\mathcal{T}_{\text{SC}}$, $\mathcal{T}_{\text{FA}}$ and
$\mathcal{T}_{\text{MD}}$ are the time duration of the three errors, and
$\mathcal{T}_{\text{Total}}$ is the total time duration.

Hungarian algorithm [21] is adopted to establish time alignment between the
hypothesis outputs and the reference transcript. In order to mitigate the
effect of inconsistent annotations and human error in reference transcript,
the Rich Transcription 2006 evaluation [20] sets a 0.25 second “no score”
collar around every boundary of the reference segment. Since this evaluation
scheme has been widely adopted in the literature [22], we follow this setup in
the challenge.

###  3.2 Multi-Speaker ASR (Track 2):

The challenge of multi-speaker ASR is to handle overlapped speech and to
recognize the content of multiple speakers, and the organizer will only
provide the Train and Eval data of AliMeeting and Aishell-4 in Track 2 as
constrained data. Certainly, the provided data of final test set (Test) is the
same as Track 1. Finally, participants are required to transcribe each
speaker, but are not required to identify a corresponding speaker for each
transcript.

The accuracy of multi-speaker ASR system in Track 2 is measured by Character
Error Rate (CER). The CER compares, for a given hypothesis output, the total
number of characters, including spaces, to the minimum number of insertions
(Ins), substitutions (Subs) and deletions (Del) of characters that are
required to obtain the reference transcript. Specifically, CER is calculated
by:

$\vspace{-0.1cm}\text{CER}=\frac{\mathcal{N}_{\text{Ins}}+\mathcal{N}_{\text{Subs}}+\mathcal{N}_{\text{Del}}}{\mathcal{N}_{\text{Total}}}\times
100\%,$ (2)

where $\mathcal{N}_{\text{Ins}}$, $\mathcal{N}_{\text{Subs}}$,
$\mathcal{N}_{\text{Del}}$ are the character number of the three errors, and
$\mathcal{N}_{\text{Total}}$ is the total number of characters. Considering
the permutation invariant training (PIT) problem, we propose two schemes to
calculate CER of the overlapping speech. First, we sort the reference labels
according to the start time of each utterance and join the utterances with the
$\left\langle\text{sc}\right\rangle$ token, which called utterance-based
first-in first-out (FIFO) method. The second methods is based on speaker,
where utterances from the same speaker are combined, and then we will
calculate all possible concatenation patterns.

###  3.3 Sub-track arrangement

For both tracks, we also set up two sub-tracks:

  * •

Sub-track I: Participants can only use constrained data to build both systems,
and the usage of extra data is strictly prohibited. In other words, system
building for Track 1 is restricted to AliMeeting, Aishell-4 and CN-Celeb, and
system building for Track 2 is restricted to AliMeeting and Aishell-4.

  * •

Sub-track II: Besides the constrained data, participants can use any data set
publicly available, privately recorded, and manual simulation for system
building. However, the participants have to clearly list the data used in the
final system description paper. If manually simulated data is used, please
describe the data simulation scheme in detail.

##  4 Baseline systems

We will release baseline systems along with the Train and Eval data for quick
start and reproducible research. For the multi-channel data of AliMeeting and
Aishell-4 recorded by far-field microphone array, we selected the first
channel to obtain Ali-far and Aishell-far, and adopted CDDMA Beamformer [23,
24] to generate Ali-far-bf, while Ali-near is the single-channel data recorded
by headset microphone. Note that the following training and evaluation in our
experiments are all based on the single-channel data (Ali-near, Ali-far, Ali-
far-bf and Aishell-far). We further use prefix Train- and Eval- to indicate
different sets, e.g., Train-Ali-far-bf denotes the one channel data outputted
by the beamformer which takes the AliMeeting 8-channel array Train data as
input.

###  4.1 Speaker diarization baseline

We adopt the Kaldi-based diarization system from the CHiME-6 challenge as the
baseline system. The diarizaiton module includes SAD, speaker embedding
extractor and clustering.

We employ the CHiME-6’s SAD [25] model111http://kaldi-
asr.org/models/12/0012_sad_v1.tar.gz, which consists of a 5-layer time-delay
neural network (TDNN) and a 2-layer statistics pooling for continuous meeting
segmentation. The feature sent into the SAD model is 40-dimensional mel
frequency cepstral coefficient (MFCC) extracted every 10 ms with 25 ms window.

The speaker embedding network 222https://github.com/BUTSpeechFIT/VBx is based
on ResNet [26] and is trained using stochastic gradient descent (SGD) and
additive angular margin loss. We ramp-up the margin during the first two
epochs and then train the network for the following epoch with a fixed margin
$m$ = 0.2. And data augmentation is performed in the same way as the SRE16
Kaldi recipe 333https://github.com/kaldi-asr/kaldi/blob/master/egs/sre16/v2.
The feature fed into the speaker embedding network is 64-dimensional mel-
filterbank which is extracted every 15 ms with 25 ms window. The post
processing result of speaker embedding is derived from probabilistic linear
discriminant analysis (PLDA) [27] model which is trained on the same data as
the speaker embedding network.

All speaker embeddings generated will be pre-clustered using agglomerative
hierarchical cluster (AHC) [28] algorithm to obtain the initial speaker
labels. The threshold of AHC algorithm is set to 0.015. The dimension of
speaker embedding is reduced from 256 to 128 for further clustering using the
Variational Bayesian HMM clustering (VBx) [29] model. Thus, the segmented
utterances from the whole meeting session together with their corresponding
speaker information.

Table 3: Speaker diarization results of meeting session with different number
of speaker on Eval set in DER (%).

[t]  #Speaker Eval-Ali-far Eval-Ali-far-bf Collar size (s) 0.25 / 0 0.25 / 0
2,3,4 20.48 / 31.47 20.98 / 32.02 2 16.40 / 24.37 17.48 / 25.72 3 14.58 /
26.01 13.81 / 26.05 4 24.85 / 36.71 25.34 / 36.91

DER is scored here with collar size of 0 and 0.25 second, but the challenge
evaluation only compares the results of 0.25 second collar size. As shown in
Table 3, since our speaker diarization system does not take overlapped speech
into consideration as we assumed that each speech frame corresponds to only
one of the speakers, DER is higher in the meeting scene with more speakers and
higher overlap ratio. Due to a large amount of interludes in the AliMeeting
data, there is lots of short-time overlap around boundary of the speaker
segments, so DER is particularly high at 0 second collar size. Considering the
high overlap rate of AliMeeting data, beamformer will suppress the voice of
the secondary speaker, resulting in poor speaker diarization performance of
the overlap segments.

###  4.2 Multi-Speaker ASR baseline

We also use a transformer-based end-to-end model [30] as our single speaker
baseline, which is based on an encoder-decoder structure with an attention
mechanism. Meanwhile, In order to extract fine-grained local features well,
our encoder block follows the same Conformer architecture as in [16, 31],
which includes a multi-head self-attention (MHSA) module, a convolution (Conv)
module, and a pair of position-wise feed-forward (FFN) module in the Macaron-
Net style. In addition, incorporating the relatively positional embeddings to
MHSA modules further improves the generalization of the model on variable
lengths of input sequences.

To obtain better recognition of multi-party meetings, we adopt the Serialized
Output Training (SOT) method in [17], which generates the transcriptions of
multiple speakers one after another. The SOT method has an excellent ability
to model the dependencies among outputs for different speakers and no longer
has a limitation on the maximum number of speakers. Remarkably, we still
employ Transformer model with conformer encoder. To recognize multiple
utterances, SOT serializes multiple references into a single token sequence.
For example, for a two-speaker case, the reference label will be given as
$R=\\{r_{1}^{1},..,r_{N_{1}}^{1},\left\langle\text{sc}\right\rangle,r_{1}^{2},..,r_{N_{2}}^{2},\left\langle\text{eos}\right\rangle\\}$,
note that $\left\langle\text{eos}\right\rangle$ is used only at the end of the
entire sequence and the special symbol $\left\langle\text{sc}\right\rangle$
represent the speaker change which is used to concatenate different
utterances. In order to avoid the complex calculation of PIT for all possible
concatenation patterns, we adopt utterance-based FIFO method which described
in Section 3.2. As the experiment shows in [17] that the FIFO method achieved
a better CER than the method of calculating all permutations. Based on this
result, we used the FIFO method for our all ASR experiments.

In all experiments, we use 71-dimensional mel-filterbanks feature as the input
of the acoustics model and frame length is 25 ms with a 10 ms shift. We use
the ESPnet [32] end-to-end speech processing toolkit to build our ASR system.
We follow the standard configuration of the state-of-the-art ESPnet Conformer
structures which contains a 12-layer encoder and 6-layer decoder, where the
dimension of attention and feed-forward layer is set to 256 and 2048
respectively. In all attention sub-layers, the head number multi-head
attention is set to 4. The whole network is trained for 100 epochs and warmup
is used for the first 25,000 iterations. We use 4950 commonly used Chinese
characters as the modeling units.

Table 4: Single speaker ASR results comparison on Eval set in CER (%).

[t]  Conformer Training data Eval-Ali-near Eval-Ali-far Eval-Aishell-far A
Train-Ali-near 18.8 56.9 32.9 B +Train-Aishell-far 18.2 46.9 28.1 C +External
data 17.0 44.8 24.7 D +External data∗ 17.2 43.4 23.7

  * •

External data includes Aishell-1 [33], AIDataTang444http://www.openslr.org/62/
and ST-CMD555http://www.openslr.org/38/.

  * •

∗: Noise and reverberation simulated.

The results of single speaker ASR are shown in Table 4, where the Train data
used by the various Conformer models are different. ConformerA is trained with
Train-Ali-near set. In order to enhance the performance of the model for the
far-field Eval set, the training of ConformerB further includes the Aishell-
far training data. Note that A and B are baselines for Sub-track I. In
addition, for ConformerC, we add additional Aishell-1 [33],
aidatatang666http://www.openslr.org/62/ and ST-
CMD777http://www.openslr.org/38/ near-field data to confirm the improvement
effect of out-domain data. Considering that the added near-field data in
ConformerC and the real far-field meeting data have a great distinction in the
room acoustics, ConformerD simulates noise and reverberation for the added
near-field data. Here C and D belong to Sub-track II where the use of external
data is not limited. Table 4 shows that ConformerD has an obvious improvement
on the far-field set, and at the same time, the performance degradation of the
near-field Eval set is very small.

Table 5: Multi-speaker ASR results on Eval set in CER (%). Conformer | Training data | Eval-Ali-far | Eval-Ali-far-bf
---|---|---|---
A | Train-Ali-near | 56.9 | 53.5
SOTA | Train-Ali-far | 43.6 | 47.0
SOTB | +Train-Ali-near | 40.8 | 43.0
SOTA_bf | Train-Ali-far-bf | 50.6 | 42.3
SOTB_bf | +Train-Ali-near | 45.3 | 39.9

The results of multi-speaker ASR are shown in Table 5, where Conformer_SOTA is
trained on the Train-Ali-far set and Conformer_SOTA_bf is trained on the Ali-
far-bf data. In order to enhance the acoustic modeling ability of the model,
we added the training data of Train-Ali-near into model B, and it can be seen
that there is a significant improvement in both Eval sets. Compared with the
single-speaker model, the SOT multi-speaker model has a significant
improvement on our multi-speaker Eval set, and SOT with Beamformer (bf) also
has positive benefits on the Ali-far-bf Eval set.

##  5 Conclusions

In this paper, we introduce the necessity of ICASSP2022 Multi-channel Multi-
party Meeting Transcription Challenge (M2MeT) and describe the datasets,
tracks, evaluation and baselines for the challenge. With the released sizable
open data and common testbed, we intend to advance meeting transcription
system and related techniques such as joint modeling of speaker diarization
and ASR through this challenge. The detailed evaluation plan, timeline, FAQs
will be updated at the challenge website: http://www.aliyun-icassp-m2met.org.

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