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REVIEW 3 major objections 5 minor 1 cited by

Rhythm Features for Speaker Identification

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Speech rhythm is a usable speaker-identity signal: a transformer trained only on character-duration sequences identifies speakers well above chance, especially in read speech.

desk verdict A clean, honest empirical study that establishes above-chance identification from character-duration sequences, but the lexical-leakage worry means the headline LibriSpeech number needs a control before it carries the rhythm interpretation. read the letter →

arxiv 2506.06834 v1 pith:FX7A3EOJ submitted 2025-06-07 eess.AS

Nick Mehlman , Thomas Thebaud , Dani Byrd , Shri Narayanan This is my paper
classification eess.AS
keywords speakeridentificationspeechrhythmframe-alignedcharactersequencestext-independentrecognitiontransformerprosodyWhisperXalignmentsx-vectorfusion
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that speech rhythm—the timing pattern of characters, phonetic units, and pauses—carries enough speaker-specific information to support speaker identification, even though most modern systems learn identity from spectrograms or waveforms instead. To test that, the authors train a transformer on frame-aligned character sequences built from automatic ASR alignments, with attention masked so the model cannot simply read the words. On LibriSpeech read speech, the rhythm-only model reaches a balanced accuracy of roughly 0.39 against a chance level of 0.0009; on VoxCeleb1 spontaneous speech it reaches 0.033 against chance 0.0008. Fusing the rhythm embeddings with x-vectors does not beat x-vectors alone on accuracy, but it does speed up early convergence. The paper concludes that rhythm is a real identity cue whose reliability drops sharply when speakers switch topics, audiences, or situations.

What carries the argument

The central object is the frame-aligned character sequence (FACS): a raw transcript converted into one character per 20 ms frame, with each character repeated for the number of frames WhisperX aligns to it and a dedicated null character marking non-speech frames. This turns timing into a sequence-modeling problem without requiring phoneme or syllable labels. The rhythm encoder is a transformer with a per-character learnable embedding, positional encoding, four to six layers of eight-headed attention, mean-pooling over time, and a linear classification head. The design choice that carries the argument is attention masking restricted to a ±2-character window, which is meant to stop the model from exploiting word- or sentence-level content so that any identity signal is durational rhythm. WhisperX supplies the time-aligned transcripts automatically, which is what makes the pipeline text-independent and applicable to unlabeled audio.

What would settle it

Train the same rhythm encoder on FACS in which the characters within each utterance are randomly shuffled while keeping every character's duration count identical, and compare balanced accuracy on the same test split. If accuracy stays near the reported rhythm-only values, the signal is genuinely durational; if it collapses toward chance, the model was reading lexical content despite the attention mask.

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Extended reading notes

Core claim

The paper's central claim is that durational rhythm can be learned directly from automatically aligned transcripts and used as a text-independent speaker identity signal. The evidence is a transformer 'rhythm encoder' trained on frame-aligned character sequences (one character per 20 ms frame, repeated for its aligned duration, with a null character for pauses): it predicts the speaker from timing alone at balanced accuracy 0.3901 on LibriSpeech (chance 0.0009) and 0.0326 on VoxCeleb1 (chance 0.0008). The authors attribute the roughly tenfold gap to the difference between read audiobook speech and spontaneous celebrity speech, arguing that ad-hoc contexts introduce high intra-speaker variability. They also claim that adding rhythm to x-vector speaker embeddings leaves final accuracy essentially unchanged for both pretrained x-vector models tested, but that the fused model converges in fewer training steps. The paper acknowledges in its limitations that word choice and sentence structure are themselves idiosyncratic, so part of the above-chance performance could come from lexical leakage rather than rhythm alone.

Load-bearing premise

The load-bearing premise is that a frame-aligned character sequence with a ±2-character attention window measures durational rhythm rather than wording, so the above-chance accuracy can be credited to timing and not to idiosyncratic word choice or sentence structure.

Editorial extensions

If this is right

  • A rhythm-only transformer identifies speakers from read speech far above chance: balanced accuracy 0.3901 versus 0.0009, so timing structure by itself is a usable identity cue.
  • Rhythm's identity value drops by an order of magnitude in spontaneous speech (0.0326 versus 0.0008 on VoxCeleb1), so high intra-speaker variability is the main obstacle to practical use.
  • Adding rhythm to a strong x-vector embedding does not raise final identification accuracy, which suggests the x-vector models already encode most of the usable rhythm information.
  • The fusion does converge faster in early training, so rhythm embeddings can act as an optimization aid even when they add no final accuracy.
  • Because the pipeline uses only alignment timing, it drops spectral and pitch details, which the paper suggests could offer privacy benefits by removing cues such as gender, age, and health status.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the leak concern in Section 6.3 is real, a decisive control would be to shuffle characters within each FACS while preserving their duration counts; near-chance accuracy would mean the model was reading words, while retained accuracy would mean it was reading rhythm.
  • The convergence-speed benefit suggests rhythm embeddings could be repurposed as an auxiliary task or warm-start for learning speaker representations, rather than only as a late fusion feature.
  • The paper's channel-robustness argument implies rhythm should help most where acoustic x-vectors degrade, such as noise, telephone bandwidth, or voice anonymization; that is a concrete setting where the null fusion result might reverse.
  • A rhythm-only representation may be comparatively privacy-preserving because it strips pitch, energy, and spectral cues that reveal gender, age, and health, but the same lexical-leak caveat means its privacy guarantee needs a separate test.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper proposes a fully automatic rhythm-based speaker identification pipeline. WhisperX extracts character-level time alignments, which are converted into frame-aligned character sequences (FACS) by repeating each character for the number of 20 ms frames it occupies and inserting a null token for non-speech frames. A transformer encoder with 4-6 layers and a ±2-character attention window is trained to predict speaker identity from FACS, and its embeddings are also fused with WavLM and SpeechBrain x-vectors. On LibriSpeech the rhythm-only model reaches 0.3901 balanced accuracy versus chance 0.0009; on VoxCeleb1 it reaches 0.0326 versus 0.0008. Fusion with x-vectors does not improve final accuracy over x-vectors alone but appears to speed convergence. The authors conclude that rhythm is a useful identity cue for read speech but degrades for spontaneous, ad-hoc speech. Section 6.3 candidly acknowledges the possibility that lexical information leaks into the model.

Significance. If the rhythm attribution were established, the result would fill a real gap: most DNN speaker recognition learns low-level spectral features, and an ASR-alignment-based rhythm stream could complement those features and offer robustness to channel degradation. The study uses two large public corpora, reports balanced accuracy rather than raw accuracy, benchmarks two x-vector extractors, and includes an honest discussion of limitations. The main limitation is that the experimental design does not isolate rhythm from lexical content; the paper's own admission of possible leakage in Section 6.3 means the central claim is not yet supported by the current experiments. Because the required control experiments are well-defined and feasible, the paper is worth revising rather than rejecting.

major comments (3)
  1. [Section 3.2 and Section 6.3] The headline LibriSpeech result (balanced accuracy 0.3901, Table 2) is attributed to rhythm, but the encoder input is an orthographic character sequence, not a duration-only representation. With six transformer layers and a ±2-character attention window, the receptive field spans roughly 25 tokens (1 + 4L, i.e., 1 + 4·6), which is sufficient to represent character n-grams and word fragments, including the null tokens that encode pause structure. The authors themselves state in Section 6.3 that 'some of this information might leak into the later layers of the model' and that word choice and sentence structure are idiosyncratic. Because LibriSpeech speakers read different audiobook passages, lexical content is a plausible confound that alone could produce above-chance identification; the much lower VoxCeleb1 accuracy (0.0326) is also consistent with a lexical explanation. A control in which character identity is removed (e.g., duration-only sequences or speaker-independent text-conditioned baselines) is required before the results can support the claim that durational rhythm drives the identification, and the term 'text-independent' should be qualified accordingly.
  2. [Section 4.2 and Table 2] All results are single-run numbers with no error bars or repeated-seed information. The model depth (4 vs 6 layers), FACS truncation (512 vs 1024 tokens), and early stopping are selected empirically on the same datasets, so the reported test accuracies are likely optimistically biased. The manuscript should report means and standard deviations over at least three random initializations and state explicitly which choices were made on a held-out development set rather than on the test split.
  3. [Section 4.3, Figure 3, and Section 6.2] The convergence-speed claim compares an x-vector-only classifier trained for at most 150 epochs with a fusion model trained for 300 epochs. Figure 3 normalizes 'steps' without specifying the batch size, number of workers or nodes, or how early stopping was applied to each curve. The faster loss decrease in the fusion model could be an artifact of the larger training budget or different optimization dynamics rather than a benefit of rhythm features. Please report matched-budget experiments, or at least compare evaluation checkpoints at the same number of steps, and provide the same information for the SpeechBrain fusion.
minor comments (5)
  1. [Throughout] Several typographical issues appear, including 'V oxCeleb1' with a spurious space in Section 4.1 and Table 2, 'e.g.,f 0' in Section 1, and '4to6' in Section 3.2; these should be fixed.
  2. [Section 5] The balanced-accuracy equation should be numbered and should explicitly state that TP_c and FN_c are computed on the test split.
  3. [Section 4.1] The number of WhisperX alignment failures is said to be 'small' but no counts are given; please report per-dataset counts and the number of speakers affected.
  4. [Figure 3] The smoothing window and the normalization procedure for 'steps' should be described in the text or caption; currently the reader cannot reproduce the curve.
  5. [Section 6.1] The phrase 'rhythm far less robust to speechcontext' has a missing space and the surrounding sentence should be reworded for clarity.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the speaker-identification results are held-out empirical measurements, and the admitted lexical-leakage risk in Section 6.3 is a confound, not a circular construction.

full rationale

The paper's derivation chain is an empirical pipeline: WhisperX produces time-aligned transcripts, these are converted to frame-aligned character sequences (FACS), a transformer is trained on FACS with a character-level attention mask, and the resulting model is evaluated on held-out test splits of LibriSpeech and VoxCeleb1 (Sections 3-5). None of the reported balanced-accuracy numbers is derived from a fitted parameter by construction; they are measured on test data against external benchmarks. The balanced-accuracy formula in Section 5 is only a metric definition. The principal threat to interpretation is stated openly in Section 6.3: "while we apply attention masking to limit the encoder's ability to exploit higher-order linguistic features, it is still possible that some of this information might 'leak' into the later layers of the model. Since word choice and sentence structure are also heavily idiosyncratic, this might engender performance benefits not accounted for by rhythmic information alone." This is an honest admission of a possible confound, and it is a validity/correctness concern rather than a circularity: the model's output is not equivalent to its input by definition, and the reported numbers could be wrong only because the input representation may carry extra identity information, not because the evaluation is self-referential. The only self-citation is [23] (Peri et al.), used in Section 6.1 for the background claim that x-vectors retain some text and speaking-rate information; this is not load-bearing for the central result. No uniqueness theorem, ansatz, or fitted predictor is imported from the authors' prior work, so the analysis is self-contained against external benchmarks. Score 0.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim rests on domain assumptions about automatic alignment quality and about isolating rhythm from lexical content; no new physical entities are introduced.

free parameters (4)
  • Attention window size (k) = +-2 characters
    Chosen in Section 3.2 because it produced the best results; affects how much local context the rhythm encoder can use and therefore what counts as rhythm versus lexical structure.
  • Transformer depth = 4 layers on VoxCeleb1, 6 layers on LibriSpeech
    Selected in Section 4.2 based on empirical performance during initial training; different depths across datasets make cross-dataset comparisons less clean.
  • FACS truncation length = 1024 tokens (LibriSpeech), 512 tokens (VoxCeleb1)
    Set in Section 4.2 to avoid memory issues; truncation removes global rhythm and speaking-rate information and differs across datasets.
  • Training epochs = 300 for joint and rhythm models, 150 for x-vector baselines
    Chosen in Sections 4.2 and 4.3; the unequal epoch budget between baseline and fusion is a confound for the convergence comparison.
assumptions (4)
  • domain assumption WhisperX time-aligned transcripts accurately reflect character-level durations, including pauses, across both datasets
    Invoked in Section 3.1 to build FACS; alignment errors, especially on VoxCeleb1 with background noise, are treated as negligible and discarded samples are not reported.
  • domain assumption Attention masking with a +-2-character window prevents the encoder from exploiting higher-order linguistic content such as words and sentences
    Section 3.2 and Section 6.3; if false, the above-chance identification accuracy may reflect idiosyncratic word choice rather than rhythm.
  • domain assumption Character-level FACS is a valid proxy for phonemic rhythm and carries no systematic spelling or lexical speaker cues
    Used throughout; unlike prior phoneme-based work [15], characters encode orthographic content, which may correlate with speaker identity in read speech.
  • domain assumption The LibriSpeech/VoxCeleb1 performance gap is caused by intra-speaker variability in spontaneous speech rather than by dataset confounds
    Argued in Section 6.1, but confounds include differing recording conditions, alignment accuracy, truncation length, and speaker population.

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Cite this review

Pith. "Pith review of Rhythm Features for Speaker Identification." pith.science (2026). https://pith.science/paper/FX7A3EOJ

@misc{pith2026250606834,
  author       = {Pith},
  title        = {Pith review of: Rhythm Features for Speaker Identification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FX7A3EOJ}},
  note         = {Machine review of arXiv:2506.06834}
}
read the original abstract

While deep learning models have demonstrated robust performance in speaker recognition tasks, they primarily rely on low-level audio features learned empirically from spectrograms or raw waveforms. However, prior work has indicated that idiosyncratic speaking styles heavily influence the temporal structure of linguistic units in speech signals (rhythm). This makes rhythm a strong yet largely overlooked candidate for a speech identity feature. In this paper, we test this hypothesis by applying deep learning methods to perform text-independent speaker identification from rhythm features. Our findings support the usefulness of rhythmic information for speaker recognition tasks but also suggest that high intra-subject variability in ad-hoc speech can degrade its effectiveness.

Figures

Figures reproduced from arXiv: 2506.06834 by the authors.

Figure 1
Figure 1. Overviews of the proposed rhythm-based speaker identification framework. then converted into a frame-aligned character sequence (FACS) with one character per frame and repetition to represent charac￾ter duration. These FACS are then used to train a transformer encoder model to predict speaker identity. We outline each of these steps in greater detail in the following sections. 3.1. FACS Extraction WhisperX extends t… view at source ↗
Figure 2
Figure 2. Rhythm encoder architecture. aid the performance of audio-based x-vector models. The x￾vectors were extracted offline and then fused with the embed￾dings from a pre-trained rhythm encoder prior to the classifi￾cation head. Both the x-vector and rhythmic embeddings were passed through linear projection layers prior to the fusion to address any misalignment between the representation spaces. During training, we jointl… view at source ↗
Figure 3
Figure 3. Training loss convergence for WavLM x-vectors only and WavLM x-vectors + rhythm features. Steps are normalized to account for different numbers of training nodes, and loss curves are smoothed using moving-average smoothing with a window of 10. 6.2. Convergence Speed While the addition of rhythmic information did not produce any benefit in accuracy over x-vectors alone, it did appear to im￾prove convergence rates, es… view at source ↗

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Multimodal Speaker Verification as a Threat to Speaker Anonymization

    eess.AS 2026-07 conditional novelty 5.0 of 10

    Multi-utterance, audio-plus-text speaker verification lowers equal error rates on anonymized speech, leaving residual speaker-identifying information after voice anonymization.

Reference graph

Works this paper leans on

32 extracted references · 27 canonical work pages · cited by 1 Pith paper

  1. [1]

    Introduction In addition to its linguistic content, human speech contains rich information about the speaker’s identity [1]. Attributes such as pitch (i.e.,f 0), spectral energy distribution, and amplitude enve- lope are highly individualistic and hence can act as useful dis- criminative features for determining who is speaking (speaker identification, SI...

    work page

  2. [2]

    For example, [11] demonstrated that human listeners were able to identify familiar speakers based on only a sinusoidal encoding of the prosodic informa- tion in speech

    Background A variety of prior works have demonstrated that the rhythmic and prosodic aspects of speech convey substantial information about the speaker’s identity. For example, [11] demonstrated that human listeners were able to identify familiar speakers based on only a sinusoidal encoding of the prosodic informa- tion in speech. Phoneme durations have b...

    work page

  3. [3]

    Rhythm Features for Speaker Identification

    Method Figure 1 summarizes our approach to rhythm-based SI. First, we use the pre-trained WhisperX model [16] to extract time- aligned transcripts from the audio file. These transcripts are arXiv:2506.06834v1 [eess.AS] 7 Jun 2025 Figure 1:Overviews of the proposed rhythm-based speaker identification framework. then converted into a frame-aligned character...

    work page Pith review arXiv 2025

  4. [4]

    Datasets We evaluate our approach on a closed-set SI task using two pop- ular speech datasets

    Experiments 4.1. Datasets We evaluate our approach on a closed-set SI task using two pop- ular speech datasets. 2 The LibriSpeech dataset [9] consists of clips from English-language audiobook recordings. Each sam- ple is roughly10−20seconds in length and is sampled at16 kHz. We used the train-500-other split, which contains around 500hours of speech data ...

    work page

  5. [5]

    Additionally, it is better suited to our closed-set identi- fication task than other commonly used measures, such as equal error rate (EER)

    Results We evaluated our models using balanced accuracy since it pro- vides a more calibrated measure of performance than standard accuracy in the case of potential class imbalances within the dataset. Additionally, it is better suited to our closed-set identi- fication task than other commonly used measures, such as equal error rate (EER). The balanced a...

    work page

  6. [6]

    Discussion Our results are consistent with prior works [2, 3] that have suggested rhythm features do convey useful information about a speaker’s identity

    Discussion and Conclusion 6.1. Discussion Our results are consistent with prior works [2, 3] that have suggested rhythm features do convey useful information about a speaker’s identity. This is evident in the fact that the rhythm-only models are able to achieve well above random- chance accuracy in predicting speaker identity. However, the large discrepan...

    work page

  7. [7]

    Durations of context- dependent phonemes: A new feature in speaker verification,

    C. J. van Heerden and E. Barnard, “Durations of context- dependent phonemes: A new feature in speaker verification,” in Speaker Classification II, ser. Lecture Notes in Computer Science, C. M¨uller, Ed. Springer, Berlin, Heidelberg, 2007, vol. 4441

    work page 2007

  8. [8]

    Prosodic pa- rameter for speaker identification,

    K. Bartkova, D. L. Gac, D. Charlet, and D. Jouvet, “Prosodic pa- rameter for speaker identification,” inSeventh International Con- ference on Spoken Language Processing, 2002

    work page 2002

Show all 32 references
  1. [9]

    Speaker recognition by machines and humans: A tutorial review,

    J. H. Hansen and T. Hasan, “Speaker recognition by machines and humans: A tutorial review,”IEEE Signal Processing Magazine, vol. 32, no. 6, pp. 74–99, 2015

    2015

  2. [10]

    We used the pre-existing identifica- tion split, which uses roughly95%of the utterances for training and the remaining5%for testing

    contains16kHz recordings of speeches of1251celebrities, totaling roughly350hours. We used the pre-existing identifica- tion split, which uses roughly95%of the utterances for training and the remaining5%for testing. Note that we intentionally selected datasets that represent ve...

  3. [11]

    Is phoneme length and phoneme energy useful in automatic speaker recognition?

    M. Igras, B. Zi ´ołko, and M. Zi ´ołko, “Is phoneme length and phoneme energy useful in automatic speaker recognition?” in XXII Annual Pacific Voice Conference (PVC), 2014, pp. 1–5

    2014

  4. [12]

    Other works, however, such as [13] have found that content can have a substantial influence on certain aspects of the speaker’s prosody

    and [4] observed that these prosodic variations tend to be fairly stable for a given speaker, regardless of the text that is being spoken. Other works, however, such as [13] have found that content can have a substantial influence on certain aspects of the speaker’s prosody. B...

  5. [13]

    Intrinsic phone durations are speaker-specific,

    H. R. Pfitzinger, “Intrinsic phone durations are speaker-specific,” inProc. 7th International Conference on Spoken Language Pro- cessing (ICSLP 2002), 2002, pp. 1113–1116

    2002

  6. [14]

    What determines duration-based rhythm measures: text or speaker?

    A. Loukina, B. Rosner, G. Kochanski, E. Keane, and C. Shih, “What determines duration-based rhythm measures: text or speaker?”Laboratory Phonology, vol. 4, no. 2, pp. 339–382,

  7. [15]

    Extraction and representation of prosodic features for language and speaker recognition,

    L. Mary and B. Yegnanarayana, “Extraction and representation of prosodic features for language and speaker recognition,”Speech Communication, vol. 50, no. 10, pp. 782–796, 2008

    2008

  8. [16]

    Analysis of speech temporal dynamics in the context of speaker verifica- tion and voice anonymization,

    N. Tomashenko, E. Vincent, and M. Tommasi, “Analysis of speech temporal dynamics in the context of speaker verifica- tion and voice anonymization,”arXiv preprint arXiv:2412.17164, 2024

    2024 arXiv

  9. [17]

    Robust speech recognition via large- scale weak supervision,

    A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever, “Robust speech recognition via large- scale weak supervision,” 2022. [Online]. Available: https: //arxiv.org/abs/2212.04356

    2022 arXiv

  10. [18]

    Lib- rispeech: an asr corpus based on public domain audio books,

    V . Panayotov, G. Chen, D. Povey, and S. Khudanpur, “Lib- rispeech: an asr corpus based on public domain audio books,” in Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on. IEEE, 2015, pp. 5206–5210

    2015

  11. [19]

    V oxceleb: a large- scale speaker identification dataset,

    A. Nagrani, J. S. Chung, and A. Zisserman, “V oxceleb: a large- scale speaker identification dataset,”Telephony, vol. 3, pp. 33– 039, 2017

    2017

  12. [20]

    On the importance of pure prosody in the perception of speaker identity,

    E. Helander and J. Nurminen, “On the importance of pure prosody in the perception of speaker identity,” 08 2007, pp. 2665–2668

    2007

  13. [21]

    Speaker idiosyncratic rhythmic features in the speech signal,

    V . Dellwo, A. Leemann, and M.-J. Kolly, “Speaker idiosyncratic rhythmic features in the speech signal,” inInterspeech 2012. Interspeech Conference Proceedings, September 2012, pp. 1–4. [Online]. Available: https://doi.org/10.5167/uzh-68554

    2012 doi

  14. [22]

    How stable are acoustic metrics of contrastive speech rhythm?

    L. Wiget, L. White, B. Schuppler, I. Grenon, O. Rauch, and S. L. Mattys, “How stable are acoustic metrics of contrastive speech rhythm?”The Journal of the Acoustical Society of America, vol. 127 3, pp. 1559–69, 2010. [Online]. Available: https://api.semanticscholar.org/CorpusI...

    2010

  15. [23]

    Speech rate normalization used to improve speaker verification,

    C. van Heerden and E. Barnard, “Speech rate normalization used to improve speaker verification,”SAIEE Africa Research Journal, vol. 98, no. 4, pp. 129–135, 2007

    2007

  16. [24]

    Phoneme duration modeling using speech rhythm-based speaker embeddings for multi-speaker speech synthesis

    K. Fujita, A. Ando, and Y . Ijima, “Phoneme duration modeling using speech rhythm-based speaker embeddings for multi-speaker speech synthesis.” inInterspeech, 2021, pp. 3141–3145

    2021

  17. [25]

    Whisperx: Time-accurate speech transcription of long-form audio,

    M. Bain, J. Huh, T. Han, and A. Zisserman, “Whisperx: Time-accurate speech transcription of long-form audio,”INTER- SPEECH 2023, 2023

    2023

  18. [27]

    Wavlm: Large-scale self- supervised pre-training for full stack speech processing,

    S. Chen, C. Wang, Z. Chen, Y . Wu, S. Liu, Z. Chen, J. Li, N. Kanda, T. Yoshioka, X. Xiaoet al., “Wavlm: Large-scale self- supervised pre-training for full stack speech processing,”IEEE Journal of Selected Topics in Signal Processing, vol. 16, no. 6, pp. 1505–1518, 2022

    2022

  19. [28]

    SpeechBrain: A general-purpose speech toolkit,

    M. Ravanelli, T. Parcollet, P. Plantinga, A. Rouhe, S. Cornell, L. Lugosch, C. Subakan, N. Dawalatabad, A. Heba, J. Zhong, J.-C. Chou, S.-L. Yeh, S.-W. Fu, C.-F. Liao, E. Rastorgueva, F. Grondin, W. Aris, H. Na, Y . Gao, R. D. Mori, and Y . Ben- gio, “SpeechBrain: A general-pu...

    2021 arXiv

  20. [29]

    Metrics for multi-class classification: an overview,

    M. Grandini, E. Bagli, and G. Visani, “Metrics for multi-class classification: an overview,”arXiv preprint arXiv:2008.05756, 2020

    2008 arXiv

  21. [30]

    Improving automatic emotion recognition from speech using rhythm and temporal feature,

    M. Bhargava and T. Polzehl, “Improving automatic emotion recognition from speech using rhythm and temporal feature,” arXiv preprint arXiv:1303.1761, 2013

    2013 arXiv

  22. [31]

    Probing the in- formation encoded in x-vectors,

    D. Raj, D. Snyder, D. Povey, and S. Khudanpur, “Probing the in- formation encoded in x-vectors,” in2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). IEEE, 2019, pp. 726–733

    2019

  23. [32]

    An empirical analysis of information encoded in disentangled neural speaker representations,

    R. Peri, H. Li, K. Somandepalli, A. Jati, and S. S. Narayanan, “An empirical analysis of information encoded in disentangled neural speaker representations,”ArXiv, vol. abs/2002.03520, 2020. [Online]. Available: https://api.semanticscholar.org/CorpusID: 211068897

    2002 arXiv

  24. [2013]

    Available: https://doi.org/10.1515/lp-2013-0012

    [Online]. Available: https://doi.org/10.1515/lp-2013-0012

    2013 doi

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