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# Towards Low-Latency Tracking of Multiple Speakers With Short-Context Speaker Embeddings

Taous Iatariene, Alexandre Guérin, Romain Serizel

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## To cite this version:

Taous Iatariene, Alexandre Guérin, Romain Serizel. Towards Low-Latency Tracking of Multiple Speakers With Short-Context Speaker Embeddings. 2025 IEEE 27th International Workshop on Multimedia Signal Processing (MMSP), Sep 2025, Beijin, China. hal-05212018

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Distributed under a Creative Commons Attribution 4.0 International License
# Towards Low-Latency Tracking of Multiple Speakers With Short-Context Speaker Embeddings

Taous Iatariene¹², Alexandre Guérin¹, Romain Serizel²

¹ Orange Innovation, Rennes, France

² University de Lorraine, CNRS, Inria, Loria, Nancy, France

taous.iatariene@orange.com, alexandre.guerin@orange.com, romain.serizel@loria.fr

**Abstract**—Speaker embeddings are promising identity-related features that can enhance the identity assignment performance of a tracking system by leveraging its spatial predictions, i.e., by performing identity reassignment. Common speaker embedding extractors usually struggle with short temporal contexts and overlapping speech, which imposes long-term identity re-assignment to exploit longer temporal contexts. However, this increases the probability of tracking system errors, which in turn impacts negatively on identity reassignment. To address this, we propose a Knowledge Distillation (KD) based training approach for short context speaker embedding extraction from two speaker mixtures. We leverage the spatial information of the speaker of interest using beamforming to reduce overlap. We study the feasibility of performing identity reassignment over blocks of fixed size, i.e., blockwise identity reassignment, to go towards a low-latency speaker embedding based tracking system. Results demonstrate that our distilled models are effective at short-context embedding extraction and more robust to overlap. Although, blockwise reassignment results indicate that further work is needed to handle simultaneous speech more effectively.

***Index Terms***—Speaker tracking, speaker embeddings, knowledge distillation, low-latency, blockwise processing

## I. INTRODUCTION

Speaker tracking refers to the task of estimating the spatial positions of the speakers present in an acoustic scene, given a multichannel audio recording. It involves performing localization for position estimation [1], but in the presence of multiple speakers, identity management considerations must be taken into account to assign estimated positions to the correct speakers [2]. Speaker tracking is often a pre-processing step to other downstream tasks, such as tele-conferencing [3], automatic speech recognition [4], and speaker separation [5], which may impose a strong low-latency constraint.

Spatial continuity is often assumed in tracking systems [2], [6], [7], which discards the scenario of intermittent speakers, who may move unpredictably during silence. This may lead to the occurrence of speakers with discontinuous spatial tracks, for which many tracking systems fail at maintaining coherent identity assignment [7], [8]. Recently, speaker embeddings were proposed to tackle the problem of tracking intermittent and moving speakers, to provide identity-related features for maintaining identity coherence over discontinuous tracks [9]. Given a tracking system, the proposed method leverages on the predicted spatial tracks to perform identity reassignment using speaker embeddings.

Speaker embeddings can be viewed as compact representations capturing information about speaker identity. A common approach for embedding extraction involves models pretrained on large-scale datasets [10], to be used in downstream tasks such as speaker diarization [11], target speaker extraction [12], and recently for speaker tracking [9]. Such pretrained, off-the-shelf models, remain sensitive to speech overlap, notably due to the absence of such scenarios in their training datasets [13]. They usually involve temporal pooling of frame level latent features [10], [14], and often require long temporal context to reach invariance to phonetic and linguistic content [15].

In consequence, Iatariene et al. [9] considers performing identity reassignment at the fragment-level, where a fragment is defined as a temporal segmentation of spatial tracks into periods of activity, thus allowing long temporal context for speaker embedding extraction. Not only does this increase the overall system's latency, but it also leads to potentially more speech overlap. This impacts negatively on embedding extraction, despite the use of beamforming to mitigate overlap [9]. Furthermore, the method assumes long term reliability from the considered tracking system, where spatial identity coherence needs to be guaranteed at the fragment level, i.e., that a fragment can be linked to only one speaker. This strong assumption may not always be verified. The longer the temporal view, the higher the probability of tracking errors such as swaps or breaks [2].

This paper aims at going towards a low-latency speaker embedding based tracking system. First, inspired by the works on other speaker embedding extractors for short duration speaker verification [16] and multi-speaker embedding extraction [17], we propose a Knowledge Distillation (KD) approach for short context speaker embedding extraction, with increased robustness to speech overlap. In KD based training, a student model is trained to reproduce the robust latent space of a teacher model pretrained on a large-scale dataset. The training framework involves providing the student model with short temporal crops of various durations, and leverages beamforming for increased robustness against overlapping speech. Second, we introduce blockwise identity reassignment, which processes over temporal blocks of fixed size. This reduces the system's latency to the chosen block duration, and removes the assumption of spatial identity coherence on fragment level.

We evaluate the efficiency of our student speaker embedding extractor, against the pre-trained teacher model, for speaker
tracking. We first use fragment-level identity reassignment [9] to assess the interest of KD in our approach. We investigate the feasibility of blockwise identity reassignment by analyzing its sensitivity to speech overlap across various block sizes, and compare its performance to the fragment-level approach.

## II. RELATED WORKS

### A. Speaker tracking

We consider a multichannel audio recording of an indoor acoustic scene (e.g, office), containing a number of speakers who may move around while speaking or when silent. Speaker tracking aims at reconstructing the time-varying position of each individual speaker present in the recording, which we refer to as *tracks*. Each track can be described by its track identity, and its set of time-varying positions.

The standard approach for tracking has long been to follow a two-step process of source localization followed by Bayesian filtering (e.g, Kalman filter), and to perform observation-to-track association in the multi-speaker case [2]. Most of the developed methods consider continuous movement during inactivity periods [6], [7], setting aside the case in which speakers move unpredictably while silent. The spatial track discontinuity induced by such scenario makes it difficult to maintain a coherent track identity over time [7], [8].

Recently, deep learning based tracking approaches have framed tracking as a task of ordered localization, relying on Permutation Invariant Training (PIT) to reach this objective [4], [18]. However, a known limit to PIT-trained models is the *block permutation problem* [19]. Calling a PIT-trained model multiple times on successive audio blocks (e.g for online processing) can cause order confusion at the inter-block level, i.e, the chosen order of predictions may vary across blocks. This makes predicted tracks coherent only at the block level. Otherwise, arbitrary permutations between speakers may affect the spatial identity coherence.

### B. Speaker embeddings

Speaker embeddings are low-dimensional features that encapsulate information about speaker identity. They are usually obtained by training a neural network over a large-scale dataset containing a great variety of speaker identities, using supervised learning through classification [10], [14], or unsupervised learning [20]. This enables the learning of a latent space in which embeddings from the same speaker are close, while embeddings from different speakers are well separated. Neural network architectures, such as x-vectors [14], usually comprise frame-level feature extractors, followed by temporal pooling for robust embedding extraction [15].

Such models perform best when the speaker embedding extraction closely matches the conditions of the training setup. Usually designed for the task of speaker verification [10], [14], [21], these models require long temporal contexts of several seconds to be invariant to phonetic and linguistic content [15]. They also remain sensitive to the presence of speech overlap, making more challenging the direct use of

<figure><img src="image_4.png" /><figcaption>Fig. 1. Overview of the Knowledge Distillation (KD) based training approach for short context speaker embedding extraction from a two speaker mixture.</figcaption></figure>

such models for applications containing overlapping speakers, such as diarization [11], [13].

## III. SHORT-CONTEXT SPEAKER EMBEDDINGS FOR TRACKING

Speaker embeddings have only recently been employed for speaker tracking, addressing the problem of spatial track discontinuity when having intermittent and moving speakers [9]. Identity-related observations become important in this context, due to the occurring of discontinuous spatial tracks.

We propose to train a speaker embedding extractor tailored for the downstream task of speaker tracking. It can therefore leverage on estimated spatial tracks that can be provided by any tracking system. We focus on the two following features: short temporal context extraction, and robustness to speech overlap. To obtain a model able to extract the speaker embedding of one of the speakers composing a mixture, given short temporal context and the prior knowledge of its spatial track, we propose a Knowledge Distillation (KD) based training approach, with an overview given in Fig. 1.

### A. Distillation based training

In KD based training, instead of directly optimizing the training towards a speaker classification task, as done usually for supervised embedding extractors training [15], we leverage on the latent space learned by a pretrained *teacher* model to provide targets for training a *student* model. Such latent space already verifies the properties of small intra-speaker and large inter-speaker distances between embeddings, and simplifies the learning objective of short context and overlap-robust embedding extraction. As such, the student speaker embeddings should ideally remain invariant to phonetic and linguistic content and be robust to the presence of noise and residual signal from a concurrent speaker, despite the short temporal context taken as input.
We consider a multichannel mixture signal $\mathbf{y} = \mathbf{x}_1 + \mathbf{x}_2 + \mathbf{n}$ of two speakers, where $\mathbf{x}_1$ and $\mathbf{x}_2$ are the reverberated (wet) speeches of the two speakers composing the mixture, $\mathbf{n}$ the noise. The speakers are described by their spatial track, $\Theta_1$ and $\Theta_2$. One mixture $\mathbf{y}$ can provide two items of training data, depending on the target speaker. We suppose in the following that the target speaker is speaker 1. We note as $\mathbf{T}(\cdot), \mathbf{S}(\cdot)$, the teacher and student models, respectively.

1) **Teacher:** The role of the teacher is to provide a training target embedding for the student model, noted $d_1$. The teacher is pretrained over a large-scale dataset with a wide range of speaker identities, to learn a robust speaker embedding latent space. Its weights are kept frozen during the KD training. The target embedding $d_1$ is obtained by leveraging on the multichannel wet speech signal $\mathbf{x}_1$ prior to mixture computation. One channel from $\mathbf{x}_1$ is chosen for embedding extraction, noted $x_1$, therefore $d_1 = \mathbf{T}(x_1)$.

2) **Student:** To predict an estimation $\hat{d}_1$ given the mixture $\mathbf{y}$, the student speaker embedding extractor relies on a beamforming algorithm $\mathcal{B}$, which is a spatial filtering technique. It leverages spatial information $\Theta_1$ to combine the multiple channels of the mixture $\mathbf{y}$ into an enhanced output $\hat{x}_1 = \mathcal{B}(\mathbf{y}, \Theta_1)$. The student speaker embedding extractor provides an estimated embedding $\hat{d}_1 = \mathbf{S}(\hat{x}_1)$, which ideally has an increased robustness to speech overlap thanks to beamforming.

## B. Temporal context

To reach the goal of embedding extraction with short temporal context, short durations need to be given to the student model during training. A random crop along the beamformed signal $\hat{x}_1$ is made prior to embedding estimation. We vary both the crop duration and crop start for each training item to ensure increased robustness and generalization to diverse temporal contexts. It is made sure that all crops contain enough activity from the target speaker, by monitoring the voice activity along the crop.

The teacher model, on the opposite, uses the whole signal duration of $\mathbf{x}_1$. This ensures optimal targets for KD based training, thanks to the use of long temporal context. The difference in duration between the teacher and student model is precisely what enables the student to learn to produce embeddings that are as robust as those of the teacher, but using shorter temporal contexts.

# IV. SPEAKER EMBEDDINGS FOR IDENTITY REASSIGNMENT

We consider the predicted tracks of any tracking system, described by their track identities and temporal positions. Identity reassignment enables the update and correction of the attributed identities, to reduce associations errors. We describe in Sec. IV-A and Sec. IV-B fragment-level [9] and the proposed blockwise speaker embedding based identity reassignment systems, respectively.

## A. Fragment-level identity reassignment

Recently, to cope with the need of long temporal pooling for embedding extraction, identity reassignment at a so-called

fragment level was proposed [9]. A fragment is defined as a temporal segmentation of spatial tracks into periods of activity, which usually ensures several seconds of temporal context for embedding estimation. Identity reassignment processes fragments by temporal order of appearance. A fragment embedding is estimated through a pretrained embedding extractor [10]. It is then compared to a pool of enrollment embeddings. These are precomputed before reassignment to ensure robustness and to represent the available speaker identities effectively. The identity of the enrollment embedding with the highest cosine similarity to the fragment embedding is assigned as the fragment's new identity.

## B. Blockwise identity reassignment

Performing identity reassignment at the fragment level has a primary noticeable limitation: it increases the latency of the system. Moreover, it assumes spatial identity coherence at the fragment level, that is, a fragment can be attributed to one speaker at most. This assumption doesn't always hold, especially with a PIT-trained neural tracker, which can suffer from the block permutation problem.

To overcome such issues, we propose to perform the identity reassignment method described in Sec. IV-A over blocks of fixed duration, which we refer to as blockwise identity reassignment. Instead of segmenting spatial tracks into fragments, this approach processes temporal blocks, sequentially in a streaming fashion, regardless of the signal content, including variations in speaker activity. Each block contains portions of estimated tracks. As previously, they are processed by temporal order of appearance within the block. One speaker embedding per portion of track is extracted, which is then compared to a pool of enrollment embeddings to take a reassignment decision.

Blockwise identity reassignment allows going towards a low-latency tracking system using speaker embeddings, where the latency is defined as the block size used for reassignment. It also relaxes the assumption of spatial identity coherence at the fragment level, although maintaining this assumption over shorter durations at the block level. Consideration must be given to the choice of the block size. Longer block sizes increase the risk of permutation errors, especially if spatial discontinuities occur within a block. Shorter block sizes reduce this risk, since the speakers are less likely to move significantly on small durations. They also reduce the system's latency. However, they result in shorter temporal contexts for embedding extraction, which is already known to degrade embedding quality, and impacts negatively identity reassignment [9].

# V. EXPERIMENTAL DESIGN

We evaluate the proposed speaker embedding extractor for its downstream application: speaker tracking, using both fragment level and blockwise identity reassignment.

## A. Datasets and metrics

The training dataset for both neural tracker and speaker embedding extraction consist in synthetic two speaker scenes
in the First Order Ambisonics (FOA) multichannel audio format [8], [9]. Items of training data are generated on-the-fly during training. Speech utterances are taken from the LibriSpeech train-clean-100 and train-clean-360 [22], forming a pool of 1172 different speaker identities.

The LibriJump dataset [8] is used for evaluation. It consists in three evaluation subsets of 150 acoustics scenes in the FOA format, of 60 s each, with a SNR of 15 dB. There is one subset per maximum number of speakers (1, 2 or 3) in the scenes. It contains intermittent speakers who change position during silences, thus having discontinuous spatial tracks. We conduct experiments on the LibriJump 1spk and 2spk subsets, which have a maximum number of speakers per scene of 1 and 2, respectively. The 1spk subset allows focusing on minimal temporal context embedding extraction without the presence of speech overlap. The more challenging 2spk subset allows for further evaluation of the robustness against speech overlap.

We use the tracking association accuracy (AssA) score, which provides an overall score to evaluate identity assignment [8], [23]. We present the mean AssA scores obtained after running a 80-20% bootstrap on the evaluation datasets. Standard deviation did not exceed 1% in all experiments.

## B. Speaker embedding extractor

We rely on state-of-the-art ECAPA-TDNN architecture for both of teacher and student models [10]. It leverages Time-Delay-Neural-Networks and Squeeze-and-Excitation modules for frame-level feature extraction, and performs attentive statistics pooling to estimate an embedding of dimension 192.

The teacher model is the Speechbrain [24] pre-trained version of this architecture<fnref n="1" />, described in particular by its number of channels which is 1024. It is trained on the large-scale datasets Voxceleb1 and Voxceleb2 [25] containing 7205 speaker identities using the Additive-Angular-Margin (AAM) softmax loss function. The student architecture mirrors the teacher's. We do experiments in which we reduce the number of channels from 1024 to 512. We train the student model from scratch or from the teacher weights, to see if it can benefit from non-random initial weights.

Similar to Cord-Landwehr et al. [17], we choose the MSE loss function as a training objective between the student's embedding prediction $\hat{d}_1$ and the teacher's target $d_1$. To assess the benefit of KD based training for short context speaker embedding extraction, we run experiments training the student models with a classification objective using the AAM-softmax loss (similar to the teacher's pretraining objective) but on the same training dataset containing beamformed crops of various durations with potentially overlapping speakers.

As for the processing of inputs during training, described in Sec. III-A1, the omnidirectional channel W of the FOA audio format is given for the teacher target embedding computation. Beamforming and random cropping of the student's inputs are performed prior to the student's embedding extraction. We use the Delay-and-Sum beamformer adapted to the FOA audio

format [26]. During training, random crop durations are set to 250, 500, 750, 1000, 1500, 2000, 8000 ms, as we empirically found that models trained only on very short inputs durations were either under-performing or not converging.

## C. Tracking system

The neural tracker is based on the source splitting approach [4]. The neural network architecture consists in a CRNN with a projected BiLSTM, which predicts splitting masks in the latent multi-speaker space, to obtain ordered position estimates on a discrete spherical grid using a PIT training approach. The maximum number of simultaneous predictions *N* is fixed by the number of output branches after splitting (two in our experiments).

## D. Identity reassignment

Identity reassignment relies on enrollment embeddings, as well as fragment or blockwise embeddings, depending on the method. We use two enrollment embeddings, for both fragment-level and blockwise identity reassignment methods, and for all the experiments, which is equal to the number of speakers present in the scenes. Enrollments are extracted following the same procedure as Iatariene et al. [9].

To remain close to the training framework, the FOA Delay-and-Sum beamformer [26] is used to extract speaker embeddings for reassignment. For the blockwise method, we vary the block sizes and therefore the temporal context for embedding extraction from short sizes (8, 25, 50 frames), to longer ones (100, 200 frames) in which the tracking system may be more prone to permutation errors. Given the frame size of 32 ms, it corresponds to 256, 800, 1600 ms and 3200, 6400 ms. Fragment embeddings are extracted given temporal contexts of 250, 750, 1500 ms, similar to Iatariene et al. [9]. We denote as whole the case where the entire fragments are used.

# VI. RESULTS

We start by studying the distilled speaker embedding extractor, using the fragment level identity reassignment system in Sec. VI-A. We move to blockwise identity reassignment in Sec. VI-B. We analyze the influence of block size on performance, and compare it to fragment-level reassignment.

## A. Short-context speaker embeddings

Tab. I displays the AssA scores obtained after performing fragment-level reassignment over the neural tracker's predictions, on the LibriJump 2spk subset. Several temporal contexts, starting from the fragment beginnings, and several embedding extractors are compared. As a reference, the baseline performance before reassignment is of 38.1 %.

After reassignment, AssA increases to 63.9 % using the teacher model and whole fragment durations. AssA reaches the highest value of 65.5 % when using the student model initialized from the teacher's weight with 1024 channels. On shorter durations, the difference is larger: using the shortest 250 ms temporal context, AssA achieves 46.8 % and 54.8 % using the teacher and student models, respectively, which corresponds to an 8 % absolute increasing.

<footnote n="1">¹https://huggingface.co/speechbrain/spkrec-ecapa-voxceleb</footnote>
<caption>TABLE I</caption>

FRAGMENT-LEVEL IDENTITY REASSIGNMENT SCORES ON THE LIBRIJUMP 2SPK DATASET. C = CHANNEL NUMBER. FS = TRAINED FROM SCRATCH. KD = KNOWLEDGE DISTILLATION. BASELINE = BEFORE REASSIGNMENT.

<table><thead><tr><th rowspan="2">Model</th><th rowspan="2">C</th><th rowspan="2">FS</th><th rowspan="2">KD</th><th colspan="3">Temporal context [ms]</th><th rowspan="2">Baseline</th></tr><tr><th>250</th><th>750</th><th>whole</th></tr></thead><tbody><tr><td rowspan="2">Teacher</td><td>1024</td><td>-</td><td>-</td><td>46.8</td><td>61.3</td><td>63.9</td><td rowspan="4">38.1</td></tr><tr><td>1024</td><td>✓</td><td>✓</td><td>53.1</td><td>58.3</td><td>61.7</td></tr><tr><td rowspan="2">Student</td><td>512</td><td>✓</td><td>✓</td><td>51.9</td><td>57.7</td><td>63.4</td></tr><tr><td>1024</td><td>✗</td><td>✓</td><td><b>54.8</b></td><td><b>63.1</b></td><td><b>65.5</b></td></tr></tbody></table>

On the following, we study the impact of several factors on the short context embedding extractor training, namely : the use of distillation, weight initialization, and model size.

1) *Interest of distillation:* Comparing the model trained with AAM-softmax to its corresponding KD one (model trained from the teacher's weights with 1024 channels, i.e., two last rows of table I), we notice that AssA goes from an absolute 15.9% drop on the shortest 250 ms temporal context, to a 25.8% drop with *whole* temporal context. This shows the interest of KD for our application, in that it simplifies the task of learning a robust latent space from minimal context.

2) *Weight initialization:* If all the student models perform better than the teacher when using short temporal context of 250 ms, the teacher's performance out passes the students most of the time when using longer contexts. We notice that models trained from scratch mainly learn to optimize performance on short temporal segments and underperform the teacher on longer contexts. The model initialized from the teacher's weights starts from a configuration suited for long temporal embedding extraction. It clearly retains the ability to process longer contexts effectively, as it achieves the best reassignment scores even when given long temporal inputs.

3) *Model size:* The last observation derived from the experiment with a smaller channel size (1024 versus 512), with random weight initialization, is more of an opening and would deserve further analysis. Nevertheless, it indicates that reducing the model size from 1024 to 512 does not have a significative impact on the performance, with the highest absolute difference being of 1.7 % using *whole* temporal contexts. This result calls for the design of a more compact architecture that could go without a significative loss of performance for the task of identity reassignment for speaker tracking.

## B. Blockwise identity reassignment

In this section, we propose to conduct experiments using blockwise instead of fragment-level identity reassignment. We retain for next the best performing student model, initialized from the teacher weights with 1024 channels. Tab. II displays the AssA scores for the blockwise identity reassignment method on LibriJump 1spk and 2spk subsets, using both student and teacher models. Several block sizes, going from 256 ms to 6.4 s are used. Scores prior to reassignment were of 55.4 % and 38.1 % on the 1spk and 2spk subset, respectively.

1) *Sensitivity to speaker overlap:* A first observation can be made about the significant drop in performance when

<caption>TABLE II</caption>

BLOCKWISE IDENTITY REASSIGNMENT PERFORMANCE ON 1SPK AND 2SPK SUBSETS FOR TEACHER AND STUDENT MODELS.

<table><thead><tr><th rowspan="2">Model</th><th rowspan="2">LibriJump subset</th><th colspan="5">Temporal context [ms]</th></tr><tr><th>256</th><th>800</th><th>1600</th><th>3200</th><th>6400</th></tr></thead><tbody><tr><td rowspan="2">Student</td><td>1spk</td><td><b>82.1</b></td><td>91.0</td><td>94.0</td><td>96.3</td><td>97.6</td></tr><tr><td>2spk</td><td><b>49.4</b></td><td><b>61.6</b></td><td><b>61.2</b></td><td><b>56.9</b></td><td>49.9</td></tr><tr><td rowspan="2">Teacher</td><td>1spk</td><td>72.3</td><td><b>92.8</b></td><td><b>96.2</b></td><td><b>98.4</b></td><td><b>99.2</b></td></tr><tr><td>2spk</td><td>40.0</td><td>54.6</td><td>56.1</td><td>55.0</td><td><b>50.0</b></td></tr></tbody></table>

dealing with two speakers as opposed to one speaker, even prior to reassignment. This highlights the high sensitivity of both models to speech overlap. On one hand, this degrades the quality of embedding extraction. On the other hand, this degrades the quality of spatial track estimation, which in turn impacts on beamforming and thus on embedding extraction.

2) *Sensitivity to block permutation problem:* It is interesting to note that, on the 2spk subset, both models exhibit bell-shape scores. After increasing, AssA decreases starting from 800 ms and 1600 ms block size for the student and teacher, respectively. This is not observable on the 1spk subset, whereas despite almost a 50-50 split of predictions over the two output branches of the tracker (as showed by an AssA score of 55.4 % prior to reassignment), the identity reassignment performance keeps increasing. The bell-shaped behavior on the 2spk subset therefore gives a clear indication about the duration from which the neural tracker is more likely to be prone to permute the speaker's tracks within a block over its two output branches. It further justifies the need for short context embedding extraction, to avoid relying on long temporal inputs, over which the tracking system may fail.

3) *Student versus teacher performance:* Comparing the student and teacher models, the same conclusion may be observed as from fragment-level experiment. Indeed, as expected and for both datasets, the student performs better on the shortest 250 ms context, while the teacher is better on the longest temporal contexts of 6400 ms. On the mid-length temporal contexts of 800, 1600 ms and 3200 ms, using the 2spk subset, we observe that the student performs better than the teacher model. This suggests that, although both models are sensitive to speech overlap (as noted in Sec. VI-B1), the student model demonstrates greater robustness in managing such overlap.

## C. Fragment-level versus blockwise identity reassignment

Tab. III compares blockwise and fragment-level reassignment using the student model. We notice that blockwise scores are inferior to fragment-level ones. This first indicates that

<caption>TABLE III</caption>

COMPARISON OF BLOCKWISE AND FRAGMENT-LEVEL IDENTITY REASSIGNMENT WITH THE STUDENT MODEL ON LIBRIJUMP 2SPK SUBSET.

<table><thead><tr><th rowspan="2">Reassignment</th><th rowspan="2">Start time</th><th colspan="3">Temporal context [ms]</th></tr><tr><th>250</th><th>750</th><th>1500</th></tr></thead><tbody><tr><td>Blockwise</td><td>-</td><td>49.4</td><td>61.6</td><td>61.2</td></tr><tr><td rowspan="2">Fragment</td><td>Beginning</td><td><b>54.8</b></td><td><b>63.1</b></td><td><b>64.8</b></td></tr><tr><td>Random</td><td>43.8</td><td>49.8</td><td>55.5</td></tr></tbody></table>
the assumption of spatial identity coherence at the fragment
level holds on, on the studied temporal contexts. Moreover,
this suggests that taking the fragments' beginnings as starting
points for embedding extraction may provide advantage for
embedding extraction, in which speech overlap is less proba-
ble. To verify this, we perform fragment-level reassignment by
taking a random start time along the fragments durations in-
stead of always starting from the beginning. The performance
is presented in the last line of Tab. III. We notice that scores
become even inferior to the blockwise reassignment method,
which confirms the previously made statement.

Indeed, the degradation of performance indicates a more
challenging input for embedding extraction, likely due to an
increased presence of speech overlap when taking a random
start along the fragment. Wrong identity reassignment deci-
sions taken on such difficult inputs will, however, have a
negative impact on the whole fragment duration. This tends to
explain the better scores observed for the blockwise method,
in which a wrong decision on one block doesn't impact the
decision on the other blocks.

VII. CONCLUSION

In this paper, we explored short temporal context speaker
embedding extraction for speaker tracking. We proposed a
Knowledge Distillation (KD) training approach to learn from a
pretrained teacher model, given short temporal inputs and us-
ing beamforming for more robustness against speech overlap.
We evaluated our distilled speaker embeddings by performing
identity reassignment over the output of a neural tracker at
the fragment level. We studied the feasibility of perform-
ing blockwise identity reassignment, to go towards a low-
latency speaker embedding based tracking system. Our results
show improved short temporal embedding extraction, with
even further improvements on longer temporal contexts when
distilling from the teacher's weights. We further illustrated an
increased robustness in the presence of overlapping speakers,
although blockwise reassignment scores suggests more room
for improvement regarding this matter. Future work can focus
on the design of lighter and more overlap-robust speaker
embeddings extractors.

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