Pith. sign in

REVIEW 3 major objections 4 minor 31 references

Robust Multi-Tier Infant-Centered Audio Understanding with Whisper via Structured Speaker Conditioning

T0 review · 3 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A family-conditioned Whisper tagger labels overlapping infant-home vocalizations at 74.88 Macro-F1.

desk verdict A solid, well-ablated engineering contribution to infant-centered audio tagging, but the per-tier victory claim overreaches the evidence from a 10-family single split. read the letter →

arxiv 2608.11587 v1 pith:ZVZITOL3 submitted 2026-08-12 eess.AS cs.CLcs.LG

Xulin Fan , Jialu Li , Mohammad Nur Hossain Khan , Kexin Hu , Bashima Islam , Mark Hasegawa-Johnson , Nancy L. McElwain This is my paper · ORCID
classification eess.AScs.CLcs.LG
keywords audiotaggingspeakerdiarizationvocalizationclassificationWhisperLoRAmulti-tierlabelingdomaingeneralizationinfant-centered
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

Naturalistic home recordings are hard to label because infant and adult vocalizations overlap, the signal-to-noise ratio is low, and every household sounds different. The paper proposes a single framewise model that labels several speaker tiers at once — child, female caregiver, male caregiver, sibling — built from a LoRA-finetuned Whisper encoder, a lightweight Transformer, and per-tier classifiers. Its central idea is a factorized speaker token, a shared tier token plus a family-specific offset that soaks up household variability during training and is set to zero for unseen families. With a temporal smoothing loss added, the model reaches 74.88 Macro-F1 and 68.14 Cohen's kappa averaged across four tiers, beating an adapted Whisper-AT system and a wav2vec-based family-audio system on the original multi-tier labels. The claim worth testing is that structured speaker conditioning, not just bigger pretrained features, is what makes the model transfer to new homes.

What carries the argument

The load-bearing object is the factorized speaker token $\tilde{s}_{\tau,f}=s_\tau+o_{\tau,f}$ in a tier token $s_\tau$ shared across families plus a learned family-specific offset $o_{\tau,f}$. The token is prepended to the downsampled Whisper embedding sequence, and a two-layer Transformer conditioned on that token produces tier-specific frame features that feed per-tier MLP classifiers. At inference on a new family the offset is zero, so only the family-invariant tier token does the conditioning. A sequence-level smoothing loss penalizes squared changes in adjacent-frame posteriors, preventing flicker inside vocalization segments, and LoRA (low-rank adaptation) fine-tunes the Whisper encoder to the home-recording domain.

What would settle it

A direct check would be to re-run the family-disjoint evaluation with several different ten-family draws from the same corpus, or bootstrap per-family metrics; if the across-tier Macro-F1 advantage over the adapted TL-TR512 baseline falls inside one standard error or flips sign on the male-caregiver tier, the generalization claim is not established.

Watch

Extended reading notes

Core claim

The paper's central claim is that one model can jointly perform speaker diarization and vocalization classification at frame level, including overlapping vocalizations, in daylong home recordings. The specific discovery is that conditioning a lightweight Transformer on a family-aware speaker token, formed as a shared tier token plus a family-specific offset, transfers to unseen households better than freezing or lightly adapting the backbone alone. On a family-disjoint split with 37 training, 5 validation, and 10 test families, the proposed model reaches across-tier averages of 74.88 Macro-F1 and 68.14 Cohen's kappa, and it outperforms both comparison systems on every tier under the original multi-tier evaluation. Ablations show that LoRA fine-tuning, the family offset, and the smoothing loss each contribute, with the largest offset effect on the male-caregiver tier.

Load-bearing premise

The strongest assumption is that ten held-out families, covering about 17 hours of annotated audio, are representative enough for the reported cross-household gains to be taken at face value.

Editorial extensions

If this is right

  • Daylong recordings from a single wearable microphone can be labeled frame-by-frame by one model, including moments when child and adult vocalize at the same time.
  • Parameter-efficient adaptation of a large pretrained speech encoder is enough for naturalistic home audio: training takes about three hours on one A100, so the approach does not require massive compute.
  • Explicitly separating family-invariant speaker categories from household-specific variability lowers cross-family bias, with the largest benefit on adult tiers where household acoustics vary most.
  • The smoothing loss is a cheap fix for temporal instability in framewise predictions, and it helps most on the child and male-caregiver tiers.
  • Because tier tokens are modular, swapping or adding tokens would let the same architecture adapt to new label sets or new speaker roles without retraining the encoder.

Reading between the lines

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

  • The design suggests that any domain-generalization task with a category prototype plus an instance offset could benefit from the same factorization; a natural test is speaker diarization in variable rooms or classrooms.
  • Because offsets are zeroed for unseen families, the model implicitly bets that every new household can be represented by the shared tier token; if a few seconds of unlabeled audio from a new home could estimate the offset, test-time adaptation might become more than the modest gain reported here.
  • The male-caregiver tier shows the largest offset effect, so a targeted experiment altering microphone placement or background noise could separate whether the offset absorbs recording-position differences rather than speaker identity.
  • The reported test-time adaptation used only entropy and min-class-confusion calibration on five epochs; a more decisive extension would compare oracle offsets with offsets inferred from short unlabeled clips, quantifying how much household-specific adaptation is actually needed.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. The paper proposes a multi-tier, framewise audio tagging system for naturalistic infant-centered recordings. It combines a LoRA-finetuned Whisper-large-v2 encoder with an MLP downsampler, a target-speaker extractor based on a Transformer conditioned on a family-aware speaker token (a shared tier token plus a learned family offset), and per-tier framewise classifiers. A sequence-level smoothing loss regularizes adjacent-frame posteriors during training. The method is evaluated on a family-disjoint split (37/5/10 families) of LittleBeats recordings across four tiers (CHN, FAN, MAN, CXN), and is compared with a Whisper-AT-derived TL-TR512 baseline and a wav2vec-based W2V-LB baseline, with ablations removing LoRA, the family offset, and the smoothing loss. A preliminary unsupervised test-time adaptation of the offsets is also reported. The manuscript claims best overall performance of 74.88 Macro-F1 and 68.14 kappa averaged across tiers, and states that the proposed method outperforms TL-TR and W2V-LB on the across-tier average and on each tier under the original multi-tier evaluation.

Significance. If the empirical claims hold, this is a useful contribution to infant-centered audio analysis: the task of jointly performing speaker-aware diarization and vocalization classification with overlapping tiers is practically important, and the proposed architecture is compact and sensible. The paper has clear strengths: a family-disjoint evaluation protocol, ablations for each design component, comparison with strong SSL-based baselines, and an explicit section on unsupervised test-time adaptation with modest but honestly reported gains. The main weakness is inferential: the headline per-tier and overall superiority claims rest on a single split of only 10 test families with no uncertainty quantification, and the baseline comparisons are not fully matched in terms of LoRA adaptation. These issues are correctable and do not undermine the plausibility of the core architecture, but they do need to be addressed before the central claims can be taken as established.

major comments (3)
  1. [Sec. 3.3 (Table 2)] The central claim that the proposed method achieves the best overall performance and outperforms the baselines on each tier rests on a single family-disjoint split with 10 test families and no error bars, confidence intervals, or repeated-seed variance. The active-label durations in Table 1 total roughly 1.38 hours for the test set (CHN 2228s, FAN 1960s, MAN 361s, CXN 412s), and the per-tier margins over W2V-LB are small on the CHN tier (Macro-F1 69.13 vs. 68.72, kappa 72.25 vs. 71.97). A family-level bootstrap or per-family evaluation is needed to verify that the reported ordering is not driven by a few atypical families; without such analysis, the statement that the method wins on each tier is not statistically supported.
  2. [Sec. 3.2 and Sec. 3.3] The TL-TR512 baseline is adapted from Whisper-AT [15], but the manuscript does not state whether this baseline also receives LoRA fine-tuning of the Whisper encoder. This matters because the proposed method's w/o LoRA ablation reaches 70.45 Macro-F1 versus TL-TR512's 69.55, while the full proposed model reaches 74.88; a substantial part of the reported improvement over TL-TR512 could therefore be due to LoRA adaptation rather than to the proposed architecture. Please specify whether TL-TR512 uses LoRA, and if it does not, add a LoRA-adapted TL-TR baseline so that the comparison is matched.
  3. [Sec. 4 and Table 2] The row Proposed+TTA reports an across-tier Macro-F1 of 74.94 and kappa of 68.24, both higher than the headline Proposed numbers (74.88 and 68.14), yet the text states that the proposed method achieves the best overall performance. If the TTA variant is meant to be excluded from the comparison, it should be explicitly marked as such (for example as a gray entry), with the reason stated; if it is not excluded, the 'best overall' claim is internally inconsistent with the table. The current table footer's 'easier evaluation setting' criterion does not obviously apply to unsupervised test-time adaptation, so the exclusion requires a clear justification.
minor comments (4)
  1. [Sec. 2.3 and Sec. 3.2] The temperature parameter T appears in the posterior definition in Eq. (2) but its value is not reported in the experiment configuration; please state the value used or clarify that it is fixed to 1.
  2. [Sec. 3.1 and Table 1] The text states there are approximately 17 hours of labeled audio, but Table 1 reports only active-label durations; the test set contains about 1.38 hours of active labels. Please clarify whether the 17-hour figure includes INACTIVE frames and report the total annotated duration per split, since this affects the interpretation of the generalization claim.
  3. [Sec. 3.3] The phrase 'on each tier' should be qualified by the original multi-tier evaluation condition, because under the overlap-removed protocol W2V-LB* actually exceeds Proposed on the CXN tier in Macro-F1 (78.95 vs. 78.87), even though that column is excluded by the gray-value convention.
  4. [Index Terms and References] There is a typo in the Index Terms ('V ocalization' should be 'Vocalization'), and reference [20] has an inconsistent URL format that should be cleaned up.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central claim is an empirical performance comparison on a family-disjoint held-out split, not a derivation fitted to its own inputs.

full rationale

The paper's central claim (Section 3.3, Table 2) is an empirical result obtained by training on a family-disjoint train/validation/test partition (Table 1) and evaluating on 10 unseen test families. No target metric is used as a training or selection signal: the checkpoint is chosen by validation kappa, and the reported test metrics are produced once after that choice. The factorized speaker token, smoothing loss, and LoRA components are all described as architectural choices with ablations, not as quantities fitted to the test results. The prior work cited as W2V-LB [6] involves overlapping authors, but it is used as a comparison baseline, not as a load-bearing justification for the proposed method's correctness; no uniqueness theorem or prior claim is invoked to forbid alternatives. The test-time adaptation section is explicitly exploratory and reports only modest gains, so it does not carry the paper's main conclusion. The small number of test families and the absence of confidence intervals are legitimate evidence-strength concerns, but they are not circularity. The derivation does not define a predicted quantity in terms of the target labels, nor does it rename a fitted parameter as a prediction. Therefore, the appropriate finding is no significant circularity, with score 0.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claim depends on a handful of hand-chosen hyperparameters and several domain assumptions about the transferability of Whisper features, the reliability of annotations, and the representativeness of the family split. No new physical entities or unverified postulates are introduced.

free parameters (5)
  • Smoothing loss weight lambda = 0.2
    Chosen by hand; balances cross-entropy and temporal smoothness. No sweep is reported.
  • Softmax temperature T = not reported (assumed 1)
    Defined in Section 2.3 with T > 0, but its value is not stated.
  • LoRA rank r = 4
    Standard small rank; not optimized.
  • LoRA scaling alpha = 8
    Set to 8; not optimized.
  • MLP window size w = 5
    Downsampling factor chosen for computational efficiency; not optimized.
assumptions (4)
  • domain assumption Whisper-large-v2 encoder provides transferable acoustic features for infant audio
    Relied on as the backbone in Section 2.2; indirectly supported by the w/o LoRA ablation.
  • domain assumption Cohen's kappa >= 0.80 for all annotation codes indicates reliable ground truth
    Used to justify label quality in Section 3.1.
  • domain assumption Family-disjoint split prevents family-level leakage
    Central to cross-family generalization claims in Section 3.1.
  • domain assumption The temporal smoothing loss acts as a regularizer without hurting boundary accuracy
    Motivates L_smooth in Section 2.3; supported only by the w/o L_smooth ablation, not by a boundary-focused analysis.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Robust Multi-Tier Infant-Centered Audio Understanding with Whisper via Structured Speaker Conditioning." pith.science (2026). https://pith.science/paper/ZVZITOL3

@misc{pith2026260811587,
  author       = {Pith},
  title        = {Pith review of: Robust Multi-Tier Infant-Centered Audio Understanding with Whisper via Structured Speaker Conditioning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZVZITOL3}},
  note         = {Machine review of arXiv:2608.11587}
}
read the original abstract

Recent advances in model design and self-supervised audio representations have improved speech and audio understanding, yet infant-centered naturalistic recordings remain challenging due to limited labeled data, low signal-to-noise ratio, and cross-family domain shifts. We present a family-conditioned, multi-tier audio tagger that combines a LoRA-finetuned Whisper encoder with a lightweight, target-speaker-aware Transformer for long-context inference and framewise prediction across tiers. To improve temporal coherence, we incorporate a simple sequence-level smoothing loss, and to enhance robustness across households, we introduce a factorized speaker-token design with a shared tier token and a learned family-specific offset, reducing family bias and promoting generalizable representations. Together, these choices enable efficient and effective infant-centered audio tagging of daylong audio recordings in home environments.

Figures

Figures reproduced from arXiv: 2608.11587 by the authors.

Figure 1
Figure 1. Training Stage of Proposed Framework 2. Methods In this section, we will outline our task formulation in section 2.1, model architecture in section 2.2, and our design of the loss functions in section 2.3. 2.1. Problem Formulation Let F be a set of families partitioned into training, validation, test families with no overlap. Each family f ∈ F has Rf audio recordings, indexed by r ∈ {1, . . . , Rf }, collected as Xf… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

31 extracted references · 22 canonical work pages

  1. [15]

    Ssast: Self- supervised audio spectrogram transformer,

    Y . Gong, C.-I. Lai, Y .-A. Chung, and J. Glass, “Ssast: Self- supervised audio spectrogram transformer,” inProceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 10, 2022, pp. 10 699–10 709

    work page 2022

  2. [1]

    Depending on the label taxonomy, the task can be formulated as speaker diarization, vocalization classifi- cation, or a combination of both

    Introduction Infant-centered audio understanding aims to characterize in- fant–adult interactions across naturalistic settings such as homes and clinics. Depending on the label taxonomy, the task can be formulated as speaker diarization, vocalization classifi- cation, or a combination of both. Early studies primarily relied on supervised neural models tai...

    work page

  3. [2]

    Robust Multi-Tier Infant-Centered Audio Understanding with Whisper via Structured Speaker Conditioning

    A sequence-level infant-centered audio tagging framework that combines a LoRA-adapted Whisper encoder, a projector, a target-speaker extractor, and tier-specific classifiers. Unlike prior approaches [7, 6], it effectively models overlapping vo- calizations from multiple family members. arXiv:2608.11587v1 [eess.AS] 12 Aug 2026

    work page Pith review arXiv 2026

  4. [3]

    A factorized speaker-token representation consisting of a shared tier token and a family-specific offset, which is designed to separate tier-level information from family- dependent variability and empirically improves cross-family performance on our evaluation split

    work page

  5. [4]

    A sequence-level auxiliary temporal smoothing loss that sta- bilizes predictions over time and promotes globally consis- tent framewise labeling. Projector Whisper Encoder LoRA Tier Token Acoustic Embeddings Target Speaker Extractor Spk Offset Tier-specific Classifier Spk Token Label Sequence Concatenated Sequence Whisper Embedding Sequence Prepend Figure...

    work page

  6. [5]

    Methods In this section, we will outline our task formulation in section 2.1, model architecture in section 2.2, and our design of the loss functions in section 2.3. 2.1. Problem Formulation LetFbe a set of families partitioned into training, validation, test families with no overlap. Each familyf∈FhasR f audio recordings, indexed byr∈{1,...,R f}, collect...

    work page

  7. [6]

    w/o LoRA

    Experiments 3.1. Dataset We conducted experiments on approximately 17 hours of la- beled audio recordings from 52 families, collected using the LittleBeats™ [19] wearable device. Infants (55 percent female) were between 4 and 15 months of age (Mean = 8.03 months). The data were partitioned into training (37 families), validation (5 families), and test (10...

    work page

  8. [7]

    In the current design, offsets are learned only for training families, and inference on new families relies solely on the shared tier tokens

    Test-Time Adaptation of the offset Although the proposed framework demonstrates strong perfor- mance across families, it does not explicitly adapt the family- specific offset parameters for unseen test households. In the current design, offsets are learned only for training families, and inference on new families relies solely on the shared tier tokens. W...

    work page

Show all 31 references
  1. [8]

    By combining a LoRA-finetuned Whisper encoder with structured speaker conditioning and tier-specific heads, the model supports overlapping speakers and framewise prediction

    Conclusion We presented a compact framework for infant-centered multi- tier audio tagging that unifies diarization and vocalization clas- sification. By combining a LoRA-finetuned Whisper encoder with structured speaker conditioning and tier-specific heads, the model supports ...

  2. [9]

    All technical content, experimental design, analysis, and scientific contribu- tions are entirely the work of the authors

    Generative AI Use Disclosure We used Claude and GPT for language editing and manuscript polishing, including improving clarity of expression, correcting grammatical errors, and formatting LATEX tables. All technical content, experimental design, analysis, and scientific contri...

  3. [10]

    For the experiments presented here, we used the Delta System at the National Center for Supercomputing Applications through AC- CESS allocations CIS240417 and CIS250040

    Acknowledgement This study was supported by funding from the National Insti- tute on Drug Abuse (R34DA050256; R01DA059422). For the experiments presented here, we used the Delta System at the National Center for Supercomputing Applications through AC- CESS allocations CIS24041...

  4. [11]

    An open-source voice type classifier for child-centered daylong recordings,

    M. Lavechin, R. Bousbib, H. Bredin, E. Dupoux, and A. Cristia, “An open-source voice type classifier for child-centered daylong recordings,” inProc. Interspeech 2020, 2020, pp. 3072–3076

    2020

  5. [12]

    Analysis of acoustic and voice quality features for the classification of infant and mother vocalizations,

    J. Li, M. Hasegawa-Johnson, and N. L. McElwain, “Analysis of acoustic and voice quality features for the classification of infant and mother vocalizations,”Speech communication, vol. 133, pp. 41–61, 2021

    2021

  6. [13]

    wav2vec 2.0: A framework for self-supervised learning of speech repre- sentations,

    A. Baevski, Y . Zhou, A. Mohamed, and M. Auli, “wav2vec 2.0: A framework for self-supervised learning of speech repre- sentations,”Advances in neural information processing systems, vol. 33, pp. 12 449–12 460, 2020

    2020

  7. [14]

    Hubert: Self-supervised speech represen- tation learning by masked prediction of hidden units,

    W.-N. Hsu, B. Bolte, Y .-H. H. Tsai, K. Lakhotia, R. Salakhutdi- nov, and A. Mohamed, “Hubert: Self-supervised speech represen- tation learning by masked prediction of hidden units,”IEEE/ACM transactions on audio, speech, and language processing, vol. 29, pp. 3451–3460, 2021

    2021

  8. [16]

    Towards ro- bust family-infant audio analysis based on unsupervised pretrain- ing of wav2vec 2.0 on large-scale unlabeled family audio,

    J. Li, M. Hasegawa-Johnson, and N. L. McElwain, “Towards ro- bust family-infant audio analysis based on unsupervised pretrain- ing of wav2vec 2.0 on large-scale unlabeled family audio,” in Proc. Interspeech 2023, 2023, pp. 1035–1039

    2023

  9. [17]

    Band- split self-supervised mamba for infant-centered audio analysis,

    X. Fan, J. Li, M. Hasegawa-Johnson, and N. L. McElwain, “Band- split self-supervised mamba for infant-centered audio analysis,” in Proc. Interspeech 2025, 2025, pp. 2795–2799

    2025

  10. [18]

    Robust self supervised speech embeddings for child-adult classification in interactions involving children with autism,

    R. Lahiri, T. Feng, R. Hebbar, C. Lord, S. H. Kim, and S. Narayanan, “Robust self supervised speech embeddings for child-adult classification in interactions involving children with autism,” inProc. Interspeech 2023, 2023, pp. 3557–3561

    2023

  11. [19]

    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,” inInternational conference on machine learning. PMLR, 2023, pp. 28 492–28 518

    2023

  12. [20]

    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

  13. [21]

    Lora: Low-rank adaptation of large language models

    E. J. Hu, Y . Shen, P. Wallis, Z. Allen-Zhu, Y . Li, S. Wang, L. Wang, W. Chenet al., “Lora: Low-rank adaptation of large language models.”ICLR, vol. 1, no. 2, p. 3, 2022

    2022

  14. [22]

    Lora-whisper: Parameter-efficient and extensible multilingual asr,

    Z. Song, J. Zhuo, Y . Yang, Z. Ma, S. Zhang, and X. Chen, “Lora-whisper: Parameter-efficient and extensible multilingual asr,”arXiv preprint arXiv:2406.06619, 2024

    2024 arXiv

  15. [23]

    Towards rehearsal-free multilingual asr: A lora-based case study on whisper,

    T. Xu, K. Huang, P. Guo, Y . Zhou, L. Huang, H. Xue, and L. Xie, “Towards rehearsal-free multilingual asr: A lora-based case study on whisper,”arXiv preprint arXiv:2408.10680, 2024

    2024 arXiv

  16. [24]

    Sparsely shared lora on whisper for child speech recognition,

    W. Liu, Y . Qin, Z. Peng, and T. Lee, “Sparsely shared lora on whisper for child speech recognition,” inICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024, pp. 11 751–11 755

    2024

  17. [25]

    Whisper-at: Noise-robust automatic speech recognizers are also strong audio event taggers,

    Y . Gong, S. Khurana, L. Karlinsky, and J. Glass, “Whisper-at: Noise-robust automatic speech recognizers are also strong audio event taggers,” inProc. Interspeech 2023, 2023

    2023

  18. [26]

    Exploring speech foundation models for speaker diarization in child-adult dyadic interactions,

    A. Xu, K. Huang, T. Feng, L. Shen, H. Tager-Flusberg, and S. Narayanan, “Exploring speech foundation models for speaker diarization in child-adult dyadic interactions,”arXiv preprint arXiv:2406.07890, 2024

    2024 arXiv

  19. [27]

    Data efficient child-adult speaker diarization with simulated conversations,

    A. Xu, T. Feng, H. Tager-Flusberg, C. Lord, and S. Narayanan, “Data efficient child-adult speaker diarization with simulated conversations,” inICASSP 2025-2025 IEEE International Con- ference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025, pp. 1–5

    2025

  20. [28]

    An embarrassingly simple approach for llm with strong asr capacity,

    Z. Ma, G. Yang, Y . Yang, Z. Gao, J. Wang, Z. Du, F. Yu, Q. Chen, S. Zheng, S. Zhanget al., “An embarrassingly simple approach for llm with strong asr capacity,”arXiv preprint arXiv:2402.08846, 2024

    2024 arXiv

  21. [29]

    Preliminary technical validation of LittleBeats™: A multimodal sensing platform to capture cardiac physiology, mo- tion, and vocalizations,

    B. Islam, N. L. McElwain, J. Li, M. I. Davila, Y . Hu, K. Hu, J. M. Bodway, A. Dhekne, R. Roy Choudhury, and M. Hasegawa- Johnson, “Preliminary technical validation of LittleBeats™: A multimodal sensing platform to capture cardiac physiology, mo- tion, and vocalizations,”Senso...

    2024

  22. [30]

    Praat: doing phonetics by computer [computer pro- gram],

    P. Boersma, “Praat: doing phonetics by computer [computer pro- gram],”http://www. praat. org/, 2011

    2011

  23. [31]

    Listen, adapt, better wer: Source-free single-utterance test-time adaptation for automatic speech recognition,

    G.-T. Lin, S.-W. Li, and H.-y. Lee, “Listen, adapt, better wer: Source-free single-utterance test-time adaptation for automatic speech recognition,” inProc. Interspeech 2022, 2022, pp. 2198– 2202

    2022

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.