# Speaker Embeddings With Weakly Supervised Voice Activity Detection For
Efficient Speaker Diarization

###### Abstract

Current speaker diarization systems rely on an external voice activity
detection model prior to speaker embedding extraction on the detected speech
segments. In this paper, we establish that the attention system of a speaker
embedding extractor acts as a weakly supervised internal VAD model and
performs equally or better than comparable supervised VAD systems.
Subsequently, speaker diarization can be performed efficiently by extracting
the VAD logits and corresponding speaker embedding simultaneously, alleviating
the need and computational overhead of an external VAD model. We provide an
extensive analysis of the behavior of the frame-level attention system in
current speaker verification models and propose a novel speaker diarization
pipeline using ECAPA2 speaker embeddings for both VAD and embedding
extraction. The proposed strategy gains state-of-the-art performance on the
AMI, VoxConverse and DIHARD III diarization benchmarks.

##  1 Introduction

Speaker diarization tries to solve the question of 'Who spoke when?' of an
audio file. Current state-of-the-art speaker diarization systems [1, 2] follow
a cascaded approach by dividing the problem statement in several subtasks,
usually consisting of voice activity detection (VAD), speaker embedding
extraction and embedding clustering. While recent end-to-end diarization
models provide a compelling and promising alternative [3, 4], current end-to-
end approaches only prove competitive with cascaded systems in a limited set
of scenarios [5].

Most VAD models rely heavily on artificially generated training data because
of the limited availability of audio with frame-level speech/non-speech
labels. For example, the VAD system provided in the popular SpeechBrain [6]
library consists of a hybrid recurrent and convolutional neural network
trained on a mixture of LibriSpeech [7] and the QUT-NOISE [8] augmentation
dataset. In a similar fashion, the winners of the diarization track of the
recent VoxCeleb Speaker Recognition Challenge (VoxSRC) relied on VoxCeleb1&2
[9, 10] mixed with MUSAN [11] augmentations to train their VAD model [1].
While attempts are made by training VAD models directly on a combination of
the available labeled diarization datasets [12, 13], performance degrades
significantly on out-of-domain test data [13].

In contrast to VAD models, speaker embedding extractors have access to a large
number of labeled speech corpora, such as VoxCeleb1&2\. Recent advances in
deep learning architectures are capable of exploiting this abundance of data,
as shown by the popular x-vector [14] and ECAPA-TDNN [15] architectures based
on time-delayed neural networks (TDNNs) or speech-adapted variations of ResNet
models [16, 17]. Recently, hybrid models combining both architectures, such as
CNN-TDNN [18] and ECAPA2 [19], have shown additional performance benefits.

In this paper, we propose a novel, weakly supervised approach for VAD which
exploits the notion that the frame-level attention mechanism of a speaker
embedding extractor shares the goal of a VAD model, i.e. indicating
intelligible speech frames. An attention mechanism allows speaker embedding
extractors to weight individual frame-level features before the temporal
pooling operation, highlighting frames containing speaker information while
suppressing non-speech or noisy frames. By interpreting the attention weights
as frame-level VAD logits, the speaker embedding extractor can act as a weakly
supervised VAD model, only relying on utterance-level speaker labels during
training. The potential benefits of this strategy are substantial compared to
a traditional supervised VAD model. It alleviates the need of an (artificial)
dataset with frame-level speech/non-speech labels, can learn from the large
availability of speaker verification corpora, reduces the computational impact
of the VAD step to zero when also used as embedding extractor for diarization
and requires no or minimal modifications to current speaker verification
architectures and training setups.

The rest of the paper is organized as follows: Section 2 describes the
derivation of VAD logits from a frame-level attention mechanism in speaker
embedding extractors. Section 3 analyzes these attention-based VAD logits from
a pre-trained ECAPA2 model to asses robustness, resolution and behavior in a
variety of speech scenarios. Section 4 uses these insights to propose a
diarization pipeline using a single ECAPA2 model for both VAD and embedding
extraction. Subsequently, Section 5 explains the experimental setup for
testing the VAD, speaker embedding and final diarization performance
separately with the corresponding results given in Section 6. Finally, Section
7 provides some concluding remarks.

Figure 1: Temporal- and channel-based attention mechanism of a speaker
embedding extractor during training. The attention responses $\boldsymbol{E}$
can be interpreted as VAD logits, resulting in a weakly supervised VAD
training setup only requiring utterance-level speaker labels.

##  2 Weakly supervised VAD

Current state-of-the-art speaker embedding extractors often employ a frame-
level attention mechanism before the temporal pooling layer [15]. The usage of
such attention systems is commonly motivated by its ability to highlight
frame-level features containing speaker information and suppressing frames
with less relevant information such as silence, noise or distorted speech. In
this section, we start with a brief overview of the temporal- and channel-
based attention mechanism used in state-of-the-art speaker embedding
extractors such as ECAPA2 and how the attention responses can be transformed
to VAD logits.

###  2.1 Temporal- and channel-based attention

The simplest form of attentive statistical pooling in speaker verification
architectures produces a singular attention scalar $e_{t}$ for each frame-
level feature $\boldsymbol{h}_{t}$ at time step $t$ in the statistical pooling
layer. This attention model can be extended to the channel dimension, as
proposed in [15], by calculating a separate scalar $e_{t,c}$ for each frame-
level feature channel $c$:

$e_{t,c}=\boldsymbol{p}^{T}_{c}f(\boldsymbol{W}\boldsymbol{h}_{t}+\boldsymbol{b})+k_{c}$
(1)

with $\boldsymbol{W}\in\mathbb{R}^{R\times C}$ and
$\boldsymbol{b}\in\mathbb{R}^{R\times 1}$ being the respective weights and
bias of an intermediate linear bottleneck layer of dimension $R$ followed by
the ReLU non-linearity given by $f(.)$. Subsequently, a linear operation with
weights $\boldsymbol{p}\in\mathbb{R}^{R\times 1}$ and bias $k$ produces the
attention scalar $e_{t,c}$ which is normalized across the temporal dimension
by applying a softmax function:

$\alpha_{t,c}=\frac{\exp(e_{t,c})}{\sum_{t}^{T}\exp(e_{t,c})}.$ (2)

with $\alpha_{t,c}$ being the normalized attention scalar used to weight the
frame- and channel-level feature $h_{t,c}$. The attentive mean pooled
statistics $\tilde{\mu}_{c}$ for each channel $c$ can now be calculated as
follows:

$\tilde{\mu}_{c}=\sum_{t}^{T}\alpha_{t,c}h_{t,c}.$ (3)

Correspondingly, the attentive standard deviation $\tilde{\sigma}_{c}$ is
given by:

$\tilde{\sigma}_{c}=\sqrt{\sum_{t}^{T}\alpha_{t,c}h_{t,c}^{2}-\tilde{\mu}_{c}^{2}}.$
(4)

The concatenation of $\boldsymbol{\tilde{\mu}}$ and
$\boldsymbol{\tilde{\sigma}}$ can subsequently be projected to a lower
dimension using a linear layer to produce the final speaker embedding
$\boldsymbol{d}$. Figure 1 depicts the temporal- and channel-based attention
mechanism during the training phase of a speaker embedding extractor.

Figure 2: ECAPA2 mean frame-level attention responses $\boldsymbol{v}$ (green)
on speech and non-speech audio inputs (blue). The $\boldsymbol{v}$ values are
capped between -0.3 and 0.3 for clarity. The red dotted line represents the
optimal speech decision threshold. Notice that the attention system behaves
similarly to a VAD model, without access to speech/non-speech labels during
training. Figure 3: Mean attention response $v_{t}$ from LibriSpeech samples
augmented with noise, music and babble at different SNR ratios. Figure 4: Mean
attention response $v_{t}$ and standard deviation of frame-level features when
transitioning from noise to speech.

###  2.2 From attention to voice activity detection

The attentive scalars in $\boldsymbol{e}_{t}$ represent the importance of the
corresponding frame-level feature located at $t$ to produce a robust speaker
embedding. Consequently, the attention system should act similarly to a VAD
model, assigning lower weights to silent, noisy and irrelevant frames while
assigning higher weights to frames with distinguishable speech. Additionally,
state-of-the-art speaker embeddings are robust against a wide variety of
background conditions, such as music, noise and babble interference, which
should enable the model to distinguish between frames with speech in
challenging conditions and true non-speech frames. To adapt the frame-level
attention vector $\boldsymbol{e}_{t}$ for the VAD task, we calculate the
average value across the channel dimension, resulting in one VAD logit $v_{t}$
for each time step:

$v_{t}=\frac{\sum_{c}^{C}e_{t,c}}{C}.$ (5)

The subsequent training setup results in a weakly supervised VAD model only
depending on utterance-level speaker labels, which is a far less strict
requirement as opposed to the need of frame-level speech/non-speech labels for
supervised VAD training. Furthermore, the VAD logits $\boldsymbol{v}$ can be
extracted simultaneously with the speaker embedding $\boldsymbol{d}$, at no
additional computational cost. This can especially be beneficial when employed
in a speaker diarization system, which will be explored in Section 4.

##  3 Attention response analysis

In this section, we analyze the attention responses of a speaker embedding
extractor to assess robustness, resolution and behavior on a variety of speech
conditions to asses the feasibility of a weakly supervised VAD model in
practical scenarios. For all analyses in this section, we use a pre-trained
ECAPA2 speaker verification model as proposed in [19] which employs the
attention mechanism described in Section 2. The model is trained using the
development partition of VoxCeleb2, which provides audio from a wide range of
background conditions and should result in a robust attention mechanism. More
details about this model can be found in the accompanying paper [19].

###  3.1 Speech and non-speech responses

Figure 2 depicts the mean frame-level attention responses $\boldsymbol{v}$
provided by the ECAPA2 model given a variety of input audio sources. We
observe that the frame-level attention responses behave similarly to a VAD
model, with higher values assigned to speech frames and lower values to
intermediate silence frames in the speech example. Noticeably, the attention
system is able to distinguish speech from non-speech audio sources,
consistently assigning low weights to frames with common background
augmentations. However, we could also observe some errors. The attention
weights have a tendency to assign higher values to frames at the onset of an
audio source, even for non-speech sounds, as depicted in the car honking
example. This leads to some occasional classification errors of a small set of
frames of non-speech sources.

###  3.2 Augmentation robustness

To assess the impact of common background audio on the ECAPA2 attention
mechanism, we depict the mean $v_{t}$ value of speech frames from 1000 random
LibriSpeech [7] utterances augmented with music, babble and noise audio at
varying signal-to-noise (SNR) levels. Only frames with speech are included to
prevent silent frames impacting the mean $v_{t}$.

We observe that noise and music augmentation behaves similarly, with a drastic
decrease in the detection of speech frames when the SNR drops below -30, at
which point the speaker becomes barely audible. Babble proves more
challenging, as the presence of other speakers quickly results in a lower
$v_{t}$ response. When interfering speakers become sufficiently prominent, the
embedding extractor will have increased difficulty to determine a dominant
speaker, ultimately assigning a low weight to the frame. As a result, the
$v_{t}$ response will likely be less useful to determine overlapping speech
frames.

Figure 5: Attention responses $v_{t}$ on TIMIT dataset, grouped by phoneme.
'spn' and 'sil' indicate speaker noise and silence, respectively.

###  3.3 Attentive region of influence

Frame-level features before the pooling operation in current state-of-the-art
speaker embedding extractors have a wide receptive field due to a large number
of stacked convolutional operations, amplified by striding or dilation. In
comparison, typical VAD models have a more narrow receptive field due to the
goal of providing frame-level speech probabilities, which can potentially be
negatively impacted if the decision is based on a too wide input region.
However, it is known that frame-level features of speaker embedding extractors
are much more inclined to be influenced by the corresponding input features at
the center of their receptive field [19].

We analyze the region of influence for the $v_{t}$ response in Figure 4, which
depicts the average $v_{t}$ value and corresponding standard deviation of
frames when transitioning from silence to speech of 1000 random LibriSpeech
utterances. We observe empirically that the region of influence of the $v_{t}$
value is on average $[t-20,t+20]$, with each frame corresponding to 10 ms of
audio, and more heavily weights central input frames. This is significantly
smaller than the corresponding receptive field of $v_{t}$ and proves favorable
for the combination of fine-grained VAD and robust utterance-level speaker
embeddings.

###  3.4 Phoneme response

A VAD model should be able to react similarly to vowels and consonants,
although the latter is more challenging due to the transient and abrupt nature
of consonants compared to vowels. To gain a better understanding on how the
attention mechanism of a speaker embedding extractor reacts to different
acoustic units, Figure 5 illustrates the average $v_{t}$ response, grouped by
phoneme, of audio from the training partition of TIMIT [20].

Figure 6: Single-step diarization with combined embedding and VAD extraction
(left) and standard two-step diarization (right).

We can observe that the attention system is significantly more inclined to put
higher $v_{t}$ values to frames containing vowels in comparison to consonants.
We suspect that vowels tend to carry more speaker-specific information. For
example, the more defined formants of vowels should provide a better
fingerprint of the vocal tract and corresponding speaker identity.
Furthermore, there is a clear distinction between voiced and unvoiced
consonants, with some unvoiced consonants given a similar speaker-identifying
weight as silence (e.g. p). However, co-articulation of unvoiced consonants
with other phonemes possibly mitigates the overall impact on practical VAD
performance.

##  4 Single-step speaker diarization

As established in the previous section, the attention system of a speaker
embedding extractor behaves similarly to a VAD model. This opens up the
possibility to use a single speaker verification model for both VAD and
embedding extraction, removing the computational overhead of the VAD phase.
Subsequently, we propose a full diarization pipeline using a single speaker
verification model for both VAD and embedding extraction, which we will refer
to as single-step diarization.

Standard cascaded diarization performs frame-level VAD and speaker embedding
extraction in separate steps, with the windowed embedding extraction done on
the detected speech segments. To enable single-step VAD and embedding
extraction, we obtain both the frame-level $v_{t}$ values and corresponding
speaker embedding using a sliding window of width $w$ and step size $s$ over
the input utterance. Subsequently, we determine the speech segments by
averaging the overlapping $v_{t}$ values and performing hysteresis
thresholding with starting and stopping thresholds $\theta_{on}$ and
$\theta_{off}$, respectively. Afterwards, speaker embeddings from windows with
no speech segments detected are discarded and not used in the clustering
phase. When speaker labels are provided by the clustering algorithm, only the
corresponding detected speech frames gets assigned to this speaker. Figure 6
depicts the difference between our single-step and standard two-step VAD and
embedding extraction.

Table 1: VAD error rate on various test sets using the attention responses of ECAPA2 in comparison with other publicly available supervised (SV) VAD models. Bold indicates best VAD performance of models not using in-domain training data. System | SV | In-domain | AMI (array) | AMI (headset) | VoxConverse | DIHARD III
---|---|---|---|---|---|---
FA | MS | VAD | FA | MS | VAD | FA | MS | VAD | FA | MS | VAD
SpeechBrain | ✓ | - | 4.3 | 1.4 | 5.7 | 3.5 | 1.5 | 5.0 | 2.2 | 1.7 | 3.9 | 17.8 | 3.6 | 21.4
Silero | ✓ | - | 3.6 | 2.3 | 5.9 | 3.3 | 1.5 | 5.2 | 2.4 | 1.9 | 4.3 | 11.0 | 9.1 | 20.1
PyAnnote 3.1 | ✓ | ✓ | 1.9 | 2.4 | 4.3 | 2.0 | 1.1 | 3.1 | 1.6 | 0.6 | 2.2 | 3.9 | 3.2 | 7.1
ECAPA2 (VAD only) | - | - | 3.0 | 2.6 | 5.6 | 3.0 | 1.9 | 4.9 | 1.2 | 1.5 | 2.7 | 8.3 | 9.9 | 18.2
Table 2: Diarization error rate on various test sets using ECAPA2 in comparison to other published non-fusion and single-scale diarization results using oracle VAD. Results are given with and without usage of the oracle number of speakers. System | AMI (array) | AMI (lapel) | AMI (headset) | VoxConverse | DIHARD III
---|---|---|---|---|---
oracle | estimate | oracle | estimate | oracle | estimate | oracle | estimate | oracle | estimate
xvec+ClusterGAN [21] | 3.6 | 2.8 | - | - | - | - | - | - | - | -
ECAPA-TDNN [22] | 2.8 | 3.0 | 2.3 | 2.5 | 1.7 | 4.0 | - | - | - | -
TitaNet-L [23] | - | - | 2.0 | 2.0 | 1.7 | 1.9 | - | - | - | -
DR-DESA [24] | - | - | - | - | - | - | - | 4.4 | - | 15.0
ECAPA2 | 2.0 | 1.8 | 1.8 | 1.7 | 1.5 | 1.4 | 4.2 | 3.8 | 13.8 | 14.1

##  5 Experimental setup

We use the same ECAPA2 model as used in Section 2 for our experimental
evaluation. VAD weights and speaker embeddings are extracted using the single-
step method as described in Section 4 with a sliding window length of 2
seconds and step size of 1 second. Hysteresis thresholding is applied to
determine the continuous speech segments. Afterwards, speech segments close
together are merged and short segments are removed. All thresholds are
determined using the development partitions of the diarization datasets.

Spectral clustering is used to group the extracted embeddings and assign
labels to the corresponding speech segments. Only speaker embeddings
containing speech in the previous step are included. The affinity matrix is
constructed using the cosine similarity of the speaker embeddings. We only
keep the 10 highest similarities for each row in the affinity matrix for
robustness. Subsequently, the eigenvectors and eigenvalues of the normalized
Laplacian, derived from the pruned similarity matrix, are calculated. To
estimate the number of speakers $n$ present in the input utterance, we use the
eigengap criterion [25]. Finally, the $n$ first eigenvectors are used for
K-means clustering to determine the speaker labels.

We use the evaluation partitions of the AMI, VoxConverse and DIHARD III
diarization datasets to asses the performance of our proposed method. The AMI
dataset consists of meeting data while VoxConverse contains multi-speaker
audio collected from YouTube. DIHARD III is composed of multiple challenging
domains, ranging from clinical interviews to telephone speech [26]. For AMI,
we follow the standard evaluation setup as described in [22] by using the
"Full-corpus-ASR partition" and exclude the TNO meetings for both the
development and test set. The audio from the microphone array is beamformed
using the open-source BeamformIt toolkit [27]. The diarization and VAD error
rate is measured on the non-overlapping speech regions while using a
forgiveness collar value of 0.25 seconds. For VoxConverse and DIHARD III, we
follow the official corresponding challenge setups [1, 26] by evaluating all
speech regions and applying a collar value of 0.25 and 0 seconds,
respectively. We note that our approach currently does not handle overlapping
speech, imposing a penalty on VoxConverse and DIHARD III results. The VAD
error rate is reported with false alaram (FA) and missed detection (MS)
breakdown.

##  6 Results

Table 1 depicts the VAD error rate on all test sets of our attention-based VAD
approach and other publicly available VAD models. Notably, we see that the
ECAPA2 VAD system gains similar or better results in comparison to supervised
models from the SpeechBrain and Silero frameworks. This indicates that the
attention mechanism of a speaker embedding extractor can act as a weakly
supervised VAD system with on par or better performance than comparable
supervised VAD models. For reference, we also included the results of the
popular PyAnnote [13] library, which outperforms all other models. However, it
is important to notice that the PyAnnote model is trained using the in-domain
development partitions of the AMI, VoxConverse and DIHARD III datasets,
resulting in significant performance increases on the corresponding test sets
[13].

To gain an individual assessment on the diarization task of ECAPA2 speaker
embeddings combined with spectral clustering, DER results using oracle VAD on
the AMI and VoxConverse test sets are available in Table 2, along with the
best published performance of current state-of-the-art models. For a fair
comparison, we only included results of non-fusion and single-scale
diarization systems. We observe that our ECAPA2-based system improves the DER
on average over the best models with 20.4% and 13.7% relative on all test
sets, respectively. This illustrates that the state-of-the-art speaker
verification performance of the ECAPA2 embeddings results in an accompanying
increase in diarization robustness. Notably, our ECAPA2 embedding extractor
achieves these results while being trained on a significantly smaller training
set than all other models in Table 2. We also observe that estimating the
number of speaker clusters using the eigengap criterion often results in
slightly better performance when compared to using the oracle number of
speakers. We noted that speaker embeddings from challenging audio conditions
tend to create an additional noisy cluster, preventing them from interfering
with the true speaker clusters. Usage of the oracle number of speakers
enforces the algorithm to assign noisy embeddings to speakers, lowering the
overall robustness of the clustering results.

##  7 Conclusion

In this paper, we provided an extensive analysis of the behavior of frame-
level attention weights of a speaker verification model. We established that
the attention mechanism acts similarly to a weakly supervised VAD system and
achieves similar or better performance as comparable supervised VAD models.
Subsequently, we proposed a diarization approach which extracts the VAD
weights and speaker embedding simultaneously using a single speaker
verification model, providing a compelling and efficient alternative to
standard cascaded speaker diarization.

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