# MULTI-SCALE SPEAKER DIARIZATION WITH NEURAL AFFINITY SCORE FUSION

###### Abstract

Identifying the identity of the speaker of short segments in human dialogue
has been considered one of the most challenging problems in speech signal
processing. Speaker representations of short speech segments tend to be
unreliable, resulting in poor fidelity of speaker representations in tasks
requiring speaker recognition. In this paper, we propose an unconventional
method that tackles the trade-off between temporal resolution and the quality
of the speaker representations. To find a set of weights that balance the
scores from multiple temporal scales of segments, a neural affinity score
fusion model is presented. Using the CALLHOME dataset, we show that our
proposed multi-scale segmentation and integration approach can achieve a
state-of-the-art diarization performance.

Index Terms—  Speaker Diarization, Uniform Segmentation, Multi-scale, Score
Fusion

##  1 Introduction

Speaker diarization aims to cluster and label the regions in the temporal
domain in terms of speaker identity. In general, the speaker diarization
pipeline consists of speech activity detection (SAD), segmentation, speaker
representation extraction, and clustering. The segmentation process largely
determines the accuracy of the final speaker label because the segmentation
determines the unit of diarization output that cannot be altered during the
clustering process. In terms of segmentation, a speaker representation faces
an inevitable trade-off between the temporal accuracy and speaker
representation quality. It has been shown in many previous studies that the
speaker representation accuracy improves as the segment length increases [1].
However, specifically in the context of speaker diarization, a longer
segmentation means a lower resolution in the temporal domain because a segment
is the unit of the process that determines the speaker identity.

In the early days of speaker diarization, the clustering process was based on
Bayesian information criterion (BIC) [2], which employs Mel-frequency cepstral
coefficients (MFCCs) as a representation for speaker traits. With BIC-based
clustering and MFCCs, speech segmentation techniques [3] with a variable
segmentation length have been employed because the benefit of having a proper
segment length for input speech outweighs the performance degradation from
variable segment lengths. This trend has changed with the increase in newer
speaker representation techniques, such as i-vector [4, 5] and x-vector [6,
7], where fixing the length of the segments improves the speaker
representation quality and reduces additional variability. For this reason,
many previous studies have made a point of compromise at 1.0 [8] to 1.5 s [7,
9] depending on the domains they target. However, a fixed segment length has
inherent limitations in terms of the temporal resolution because the
clustering output can never be finer than the predetermined segment duration.

Fig. 1: Trade-off curve between fidelity of speaker representations and
temporal resolution.

Therefore, we propose a scheme that addresses the problem arising from such a
trade-off and applies a new segmentation approach. The proposed method employs
a multi-scale diarization solution where affinity scores from multiple scales
of segmentation are fused using a neural score fusion system. The graph in
Fig. 1 shows the trade-off between segment length and fidelity of speaker
representations from two segments. Our goal is for our system to be located on
the graph above the trade-off curve with a higher temporal resolution while at
the same time achieving a superior accuracy of the affinity measure.

There have been few studies related to the problem discussed herein. In terms
of speaker embedding extraction, few studies have employed a multi-scale
concept for speaker embedding [10, 11] targeting short utterance lengths.
These studies apply multi-scale aggregation [10] or multilevel pooling [11] in
the feature level in the neural network models. Because our proposed neural
network model does not generate speaker embeddings, feature-level multi-scale
approaches are far from our focus.

By contrast, there are a few studies in which diarization systems aggregate
the output of multiple modules. In [12], the authors employed a majority
voting scheme on multiple segmentation streams. In [13], the authors
introduced a cluster matching procedure that can integrate multiple
diarization systems. In addition, in [14], a diarization output voting error
reduction (DOVER) was presented for improving the diarization of a meeting
speech. These previous studies deal with either a feature-level multi-scale
concept of a neural network [10, 11] or a diarization system integration [12,
13, 14], whereas our proposed method focuses on the score fusion of multi-
scale speech segments.

Our proposed approach has the following novelties. First, unlike conventional
varying-length speech segmentation or single-scale segmentation modules, our
system employs multiple discrete segment lengths and proposes a method to
integrate the given scales. Second, the proposed method can attentively weigh
the affinity from multiple scales depending on the domain and characteristics
of the given speech signal. This distinguishes our work from approaches that
require fusion parameters to be manually tuned on a development set. [15, 16].
In addition to these novelties, our proposed multi-scale approach outperforms
a single-scale diarization system and achieves a state-of-the-art performance
on the CALLHOME diarization dataset.

The remainder of this paper is structured as follows. In section 2, we
introduce the segmentation scheme. In section 3, we introduce the proposed
network architecture and how we train the proposed neural network. In section
4, we show the experimental results on various datasets and evaluation
settings.

##  2 Multi-Scale Diarization System

###  2.1 Overview of the proposed system

Fig. 2 shows a block diagram of our proposed method as opposed to the
conventional speaker diarization pipeline. For embedding the extractor, we
employ an x-vector in [6, 17]. We replace the segmentation process with a
multi-scale segmentation process followed by a neural affinity score fusion
(NASF) system, which will be described in the following sections. The NASF
module outputs an affinity matrix similar to that in a conventional speaker
diarization framework. In our proposed diarization, we employ the clustering
method presented in [18].

###  2.2 Multi-scale segmentation

Our proposed segmentation scheme for each scale is based on the segmentation
scheme that appeared in a previous study [7, 17]. Fig. 3 shows how our
proposed multi-scale segmentation scheme works. Although many different scale
lengths and numbers of scales can be adopted, we employ three different
segment lengths: 1.5, 1.0, and 0.5 s. The hop-length is half the segment
length, which is 0.75, 0.5, and 0.25 s, respectively. In addition, the minimum
segment length of the each scale is set to 0.5, 0.25, and 0.17 s,
respectively.

We refer to the finest scale, 0.5 s, as the base scale because the unit of
clustering and labeling is determined by this scale. For each base scale
segment, we select and group the segments from the lower temporal resolution
scales (1.0 s and 1.5 s) whose centers are the closest to the center of the
base scale segment. This mapping is shown by the red bounding boxes in Fig. 3.
By selecting the segments as in Fig. 3, the clustering results are generated
based on the base scale segments, whereas measuring the affinity for the
clustering process is achieved using the distance obtained from all three
scales.

Fig. 2: Example of multi-scale segmentation scheme.

Fig. 3: Example of multi-scale segmentation and mapping scheme.

##  3 Neural Affinity Score Fusion Model

For the speaker diarization task, learning an affinity fusion model is not a
straightforward downstream task unlike training speaker embedding from speaker
labels because the diarization output is obtained through a clustering
(unsupervised learning) approach. Thus, we derived an indirect method that can
learn a model for estimating the desirable weights for the affinity scores
from multiple scales.

###  3.1 Creating Data Labels

To represent the ground-truth composition of the speakers in the given
segments, we employ a concept of a speaker label vector based on the duration
of each speaker. The dimensions of the speaker label vector are determined
based on the total number of speakers in a session. Fig. 4 shows an example of
how we create labels of training data. Let segments A and B be a pair of
segments for which we want to obtain an affinity score label. In Fig. 4, the
speaker label vector $v_{A}$ obtains values of $(0,0.5)$ and $(0.5,0.25)$ from
the duration of the speaker labels from segments A and B, respectively.

Because the speaker label vectors are always positive, the ground truth cosine
distance value ranges from zero to one. To match the range, the cosine
similarity value from the speaker embeddings are min-max normalized to the (0,
1) scale. In total, for $L$ segments in the given session, we obtain
$\,{}_{L}C_{2}$ ground truth affinity score labels, which were created for the
base scale, which has a segment length of 0.5 s.

Fig. 4: Example of training data label generation.

###  3.2 Affinity Score Fusion Networks

To tackle the affinity weighting task, we employ a neural network model
optimized using the mean square error (MSE) between the ground truth cosine
similarity $d$ and weighted cosine similarity value $y$. We expect the
estimated weight to minimize the gap between the ideal cosine similarity and
the weighted sum of the given cosine similarity values $(c_{1},c_{2},c_{3})$.
To achieve this, we employ an architecture similar to that of a Siamese
network [19], which shares the weights of the networks to process the two
different streams of information. Thus, we build a neural network model that
can capture the non-linear relationship between a set of affinity weights and
a pair of speaker representations by setting up a pair of cloned neural
networks.

Fig. 5 shows the architecture of the affinity score fusion network. After the
multi-scale segmentation process, embeddings for each scale are extracted with
three different embeddings for the three scales. The set of embeddings
(segment set A) are then processed using three parallel multi-layer
perceptrons (MLPs) and the output of the MLPs are merged to form an embedding
from all three scales. The forward propagation of the input layer to the
merging layer is also applied to another set of segments (segment set B) to
obtain a merged embedding for this set. After forward propagation of two
streams of input, the difference between two merged embeddings are passed to
the shared linear layer, which outputs the softmax values. We then take the
mean of the softmax values from $N$ input pairs.

$\textbf{w}=\Bigg{(}\frac{1}{N}\sum_{n=1}^{N}w_{1,n},\frac{1}{N}\sum_{n=1}^{N}w_{2,n},\frac{1}{N}\sum_{n=1}^{N}w_{3,n}\Bigg{)}$
(1)

The set of averaged softmax values
$\textbf{w}=(\bar{w}_{1},\bar{w}_{2},\bar{w}_{3})$ weights the cosine
similarity values, $\textbf{c}=(c_{1},c_{2},c_{3})$, which are calculated
using the speaker representations to obtain the weighted cosine similarity
value as follows:

$y_{n}=\sum_{i=1}^{3}\bar{\omega}_{i}c_{i,n}=\textbf{w}^{T}\textbf{c}_{n}$ (2)

where $y_{n}$ is the output of the affinity weight network for the $n$-th pair
out of $N$ pairs. Finally, the MSE loss is calculated using the ground truth
cosine similarity value $d$ as follows:

$\pazocal{L}(\textbf{y},\textbf{d})=\frac{1}{N}\sum_{n=1}^{N}(y_{n}-d_{n})^{2},$
(3)

where $d_{n}$ is the $n$-th ground-truth cosine score for the $n$-th pair of
segments. In inference mode, we also take the mean of $N$ sets of softmax
values to obtain a weight vector w.

Fig. 5: Neural multi-scale score fusion model

###  3.3 Weighted Sum of Affinity Scores

Our proposed system estimates a weight vector w for each input session (an
independent audio clip under a real-world scenario). For inference of the
affinity weight, we randomly select $N$=$5\cdot 10^{5}$ samples out of
$\,{}_{L}C_{2}$ pairs per session, which has $L$ base scale segments, and
weigh the given affinity matrices as indicated in Fig. 6. The weighted
affinity matrix is then passed to the clustering module.

###  3.4 Affinity Matrix and Clustering

In our previous study [18], we showed that the cosine similarity when applying
the NME-SC method can outperform that of the prevailing clustering approaches,
such as a probabilistic linear discriminant analysis (PLDA) coupled with
agglomerative hierarchical clustering (AHC). Thus, we employ cosine similarity
and NME-SC method in [18] to verify the efficacy of the proposed multi-scale
affinity weight model by showing the additional improvement from the results
in [18]. In addition, we compare the performance with systems based on single-
scale segmentation methods.

##  4 EXPERIMENTAL RESULTS

###  4.1 Datasets

####  4.1.1 CALLHOME (NIST SRE 2000)

NIST SRE 2000 (LDC2001S97) is the most widely used diarization evaluation
dataset and is referred to as CALLHOME. To compare its performance with
performance of previous studies [6, 17], a 2-fold cross validation is
conducted for evaluation on CALLHOME for AHC coupled with the PLDA method.

####  4.1.2 Call Home American English Speech (CHAES)

CHAES (LDC97S42) is a corpus that contains only English speech data. CHAES is
divided into train (80), dev (20), and eval (20) splits.

####  4.1.3 AMI meeting corpus

The AMI database consists of meeting recordings from multiple sites. We
evaluated our proposed systems on the subset of the AMI corpus, which is a
commonly used evaluation set that has appeared in numerous previous studies,
and we followed the splits (train, dev, and eval) applied in these studies
[20, 21, 22].

Fig. 6: Example of weighted sum of affinity matrices

###  4.2 Training of NASF network

For the training of our proposed neural network model, we use the CHAES- and
AMI-train splits. We also apply the CHAES-dev and AMI-dev sets to tune the
hyper-parameters of the network. We use MLPs with 2 hidden layers and 128
nodes each and apply the Adam optimizer with a learning rate of 0.001.

###  4.3 Distance measure and clustering algorithm

In this paper, all systems employ a speaker embedding extractor (x-vector)
that appeared in [6, 17]. The following baselines are for the distance measure
and clustering method.

####  4.3.1 PLDA+AHC

This approach is based on the AHC algorithm coupled with the PLDA as it
appeared in [6, 17]. The stopping criterion of the AHC was selected based on a
grid-search for each development set. We use the PLDA model provided in [17].

####  4.3.2 COS+NME-SC

As stated in [18], NME-SC does not require a development set to tune the
clustering algorithm. We use the same set of segments and speaker embeddings
as PLDA+AHC but replace the distance measure with NASF from three different
scales and replace the clustering algorithm with NME-SC. In this study, we do
not evaluate combinations such as PLDA+NME-SC or COS+AHC because such
combinations of algorithms have under-performed PLDA+AHC and COS+NME-SC [23].

Table 1: Experimental results of baselines and the proposed methods | Number of | PLDA+AHC | Previous | COS+NME-SC | Multi-scale COS+NME-SC
---|---|---|---|---|---
Dataset | Sessions | $0.5s$ | $1.0s$ | $1.5s$ | Studies | $0.5s$ | $1.0s$ | $1.5s$ | Equal Weight | NASF-D | NASF-S
AMI | 12 | 38.42 | 20.07 | 10.55 |  8.92 [20] | 26.96 | 9.82 | 3.37 | 6.51 | 3.89 | 3.32
CHAES-eval | 20 | 4.58 | 3.15 | 3.28 |  2.48 [18] | 8.71 | 3.35 | 2.48 | 2.52 | 2.47 | 2.04
CALLHOME | 500 | 17.89 | 9.13 | 8.39 |  6.63 [24] | 20.96 | 7.81 | 7.29 | 6.64 | 7.02 | 6.46
|  |  |  |  |  |  |  |  |  |  |

###  4.4 Diarization evaluation

####  4.4.1 Inference Setup

  * •

Equal Weight: This system is evaluated to show the efficacy of the NASF method
over naive cosine similarity averaging. An equal weight system does not use
any inference and applies equal affinity weights
$(\frac{1}{3},\frac{1}{3},\frac{1}{3})$ for all sessions in all datasets.

  * •

NASF-D: This system divides the input session into three equal-length sub-
sessions and estimates six different affinity weight vectors (w) for the six
different affinity matrices ($3!=6$), which are intra-sub-session (three
sessions) and inter-sub-session (three sessions) affinity matrices. Finally,
we calculate the weighted sum of these matrices and join the affinity matrices
to cluster the integrated matrix as a single affinity matrix.

  * •

NASF-S: This system estimates a set of affinity weights for an entire session.
Thus, we have one affinity weight vector w for each session, and the entire
affinity matrix is weighted using this single weight vector.

####  4.4.2 DER calculation

To gauge the performance of speaker diarization accuracy, we use oracle SAD
output that excludes the effect of SAD module. For all evaluations and
datasets, we estimate the number of speakers in the given session without
additional information about speaker numbers. We employ an evaluation scheme
and software that appeared in [25] to calculate Diarization Error Rate (DER).

Fig. 7: Plot of affinity weights by datasets.

###  4.5 Discussions

We compare the DER obtained from the proposed method with the DER values
obtained from each segment scale. Table 1 shows the DER from numerous settings
and datasets. We show the DER values of the PLDA+AHC approach for three
different segment scales (1.5, 1.0, and 0.5 s) and how the performance of the
diarization changes with the distance measure and clustering method. We also
list the lowest DER value that we could find that has appeared in a published
paper on speaker diarization [20, 24], including the CHAES-eval results of our
previous study [18].

Most importantly, we compare the COS+NME-SC methods with segment lengths of
0.5, 1.0, and 1.5 s with the proposed method. The best performing system,
NASF-S, obtains relative improvements with error rates of 1.5%, 17.3%, and
11.4% for AMI, CHAES-eval, and CALLHOME, respectively, over the 1.5-s COS+NME-
SC baseline. For the AMI corpus, the improvement was minor whereas the
CALLHOME and CHAES-eval sets showed a significant improvement given that the
DER result from COS-NME-SC with 1.5-s segments is already competitive compared
to the results appearing in previous published studies. In Fig. 7, we show the
ranges and averages of the estimated weights over the sessions in each
dataset. We can see that only the CALLHOME dataset shows a range that includes
equal weight within the weight range for 1.0-s segments, whereas the weight
ranges from AMI and CHAES-eval show no overlap with an equal weight. We
conjecture that this is related to the result in which an equal weight shows
an improvement for only CALLHOME.

From the experiment results, we can obtain a few valuable insights. The equal
weight experiment gives conflicting results for AMI and CALLHOME.
Nevertheless, from the equal weight experiment, we can verify that the
desirable affinity weight cannot be simply found by averaging it and that the
NASF approach can be a solution for estimating the desirable weights.

The difference in performance gains between AMI and CALLHOME also shows the
characteristics of a multi-scale approach. Because the longest segment we
employ in our system is 1.5 s, we can argue that the DER reduction comes from
the higher resolution of the segments. This becomes clear if we compare the
proposed method with the DER we obtain from 0.5-s segments. However, the gain
from our proposed method was not that significant with the AMI corpus. We
speculate that this is caused by the characteristics of the dataset because
the average length of the continuous speaker homogeneous region in the AMI
corpus is 2.56 s, whereas the lengths for CALLHOME and CHAES are 2.17 and 2.07
s, respectively. In this sense, we can argue that the CALLHOME and CHAES
datasets are more likely to benefit from the proposed multi-scale diarization
approach because a higher resolution can capture shorter speaker homogeneous
regions.

Another important finding obtained from this study is that varying the
affinity weights in a session (i.e., a diarization session that is being
clustered) does not lead to a good performance. Having a constant affinity
weight in a single affinity matrix leads to a better performance, as we can
see from the NASF-S outperforming NASF-D.

##  5 Conclusions

We proposed a method that mitigates the limit of the trade-off between the
temporal resolution and the fidelity of the speaker representations. The
proposed method estimates a set of weights that minimizes the gap between the
weighted sum of the cosine affinity and the ground-truth affinity between a
pair of segments. The proposed NASF system has a temporal resolution of 0.5 s
and improves the diarization performance over conventional single-scale
systems, achieving a state-of-the-art performance on the CALLHOME dataset. We
believe that the proposed multi-scale score fusion approach on a diarization
task can achieve a breakthrough in such research.

Further studies will include an online version of multi-scale speaker
diarization where we can find the weight of the affinities in an online
fashion. We expect our proposed multi-scale diarization framework to be
applicable to many different diarization studies because our method is
compatible with other modules in the diarization pipeline.

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