AI-Guided Learning: Research on Knowledge and Skill Acquisition Support Methods Using Deep Learning Audio-Video Processing Techniques

Kazuki Kawamura

REVIEW 4 major objections 6 minor 231 references

AI-Guided Learning: Research on Knowledge and Skill Acquisition Support Methods Using Deep Learning Audio-Video Processing Techniques

T0 review · 4 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read This dissertation claims that deep-learning systems at three learning stages—phoneme-level adaptive playback, multimodal video summarization, and unlabeled-data pronunciation feedback—can make audio-visual learning faster and more…

desk verdict A three-system dissertation with a genuinely novel AIxSpeed core, but the load-bearing evaluation of that core is partly circular and under-tested; it deserves review, not rejection. read the letter →

arxiv 2608.08990 v1 pith:56IP7E6O submitted 2026-08-10 cs.HC cs.MM

Kazuki Kawamura This is my paper · ORCID

classification cs.HCcs.MM

keywords AI-guidedlearningadaptiveplaybackspeedASRconfidencephoneme-levelcontrolvideosummarizationvoicecloningpronunciationfeedbackself-supervisedspeech

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 dissertation takes on two problems that make audio and video frustrating learning media: consuming long-form content sequentially takes too long, and imitation-based practice lacks scalable feedback. It proposes a Consume–Understand–Imitate framework and evaluates one deep-learning system for each stage. The central empirical claims are that AIxSpeed achieves an average playback factor of about 1.3x at higher listener opinion scores than matched constant-speed audio, that FastPerson cuts lecture-video viewing time by 53% with no statistically significant quiz-score difference, and that Profy produces a larger observed pronunciation-intelligibility gain than imitation-only practice, with non-overlapping pre- and post-practice confidence intervals. The dissertation presents these results as establishing an integrated technical basis for AI-guided learning from audio-visual content. If the results hold, learners could reclaim a large fraction of listening and viewing time and receive localized practice feedback without measured loss of comprehension.

What carries the argument

The object carrying the speed argument is the shared Wav2Vec2-based network in AIxSpeed, trained with Loss = Lossspeed + λLossctc: one head outputs a per-segment playback factor and the other performs CTC speech recognition, so a single model decides how fast each phoneme can go and verifies that the accelerated speech stays recognizable. The object carrying the summarization argument is FastPerson's video-to-video pipeline: chapter segmentation from scene and silence detection, visual metadata from OCR and object detection, transcription via Whisper, LLM-generated summaries, and VITS voice cloning to keep the narrator's voice continuous with the original. The object carrying the feedback argument is Profy's classifier, trained on unannotated learner and native speech, which scores utterances and visualizes the waveform regions the classifier attends to plus distances in latent space.

What would settle it

Take the same sentence materials from LibriSpeech and UME-ERJ, run AIxSpeed's per-phoneme speed profile, and have native and non-native listeners transcribe the output at word or phoneme level. If human accuracy does not track ASR confidence in those phoneme-level segments, or if the r≈0.998 correlation from the sentence-level pilot drops for non-native speech, then the claimed intelligibility preservation is unsupported. A second check: a quiz-based replication of FastPerson on unseen lectures with summary-only viewing would test whether the 53% time saving hides comprehension differences beyond the two videos used.

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Extended reading notes

Core claim

The central claim is that speech-recognition confidence is a usable proxy for human listening difficulty at speeds above 1.0x, and that this proxy, a multimodal summarizer with voice preservation, and a self-supervised proficiency model each carry their respective stage of the learning cycle. The pilot study reports a correlation of r=0.9977 between human transcription accuracy and ASR accuracy across the tested speeds, and the full system is trained with a combined loss that maximizes speed while minimizing CTC recognition error, giving average playback factors of 1.30x on LibriSpeech and 1.29x on UME-ERJ with higher mean opinion scores than matched constant-speed playback. FastPerson is claimed to reduce viewing time by 53% on educational videos with quiz scores statistically indistinguishable from normal playback, and Profy is claimed to improve pronunciation intelligibility with pre- and post-practice confidence intervals that do not overlap the elicited-imitation baseline. The paper frames these three results as components of an integrated AI-Guided Learning loop rather than as standalone tools.

Load-bearing premise

The speed-optimization system stands or falls on the assumption that a speech recognizer's confidence remains a reliable proxy for human listening difficulty when applied at phoneme granularity to speech outside the tested English corpora, including non-native speech; the paper validates the proxy only at coarser granularity and for the specific datasets.

Editorial extensions

If this is right

Learners using AIxSpeed would get roughly 23% shorter listening sessions at the same average quality, since 1.3x playback turns a 60-minute lecture into about 46 minutes.
Video platforms could offer per-chapter summary versions that halve viewing time while quiz-measured comprehension stays statistically unchanged, with learners able to drill into full chapters when needed.
Pronunciation practice with model-derived localized feedback can improve intelligibility without requiring transcribed error annotations, lowering the cost of building feedback systems for new languages or skills.
The three systems compose a single learning loop: consume faster, understand via summaries or full segments, imitate with feedback, then return to relevant segments.

Reading between the lines

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

A direct consequence the author leaves implicit is that AIxSpeed's speed profile could be personalized by raising or lowering the intelligibility threshold; the paper notes personalization as future work but does not test it.
Because the pilot correlation was measured at utterance and sentence level, the strongest untested extension is the assumption that the same proxy holds at phoneme boundaries; a per-phoneme human transcription study would settle it.
FastPerson's fixed summary-length weights (0.3 for audio, 2.5 for visual) suggest a testable extension: adapting those weights to learner proficiency or content type could yield further time savings or comprehension gains, which the dissertation does not test.
The same Profy mechanism—a classifier trained on unannotated expert versus learner data with region highlighting—could transfer to other imitation domains such as music or movement, though the paper evaluates only English pronunciation.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Three systems, one dissertation, and the whole is worth a look even though none of the three headline claims is airtight. The genuinely new pieces are phoneme-level playback-speed control driven by ASR confidence, video-to-video summarization that keeps the original speaker's voice, and a pronunciation-feedback system that learns from unannotated speech. Each system is described in enough detail that I could reimplement the pipeline, and the dissertation is unusually candid about what its evaluations do and do not show.

The softest spot is AIxSpeed. The stress-test note is right: the pilot survey validates an aggregate correlation between human and ASR transcription error across constant speeds, but the deployed system makes per-phoneme decisions, and the pilot does not test that granularity. The technical evaluation uses the same Wav2Vec2 recognizer family that was part of the training objective, so the CER/WER advantage is partly circular. And the MOS gain over matched constant-speed playback is not significance-tested, by the author's own admission. The paper is transparent about all of this, but the abstract and framing still lean on the stronger reading. FastPerson is the most solid: a 53% viewing-time reduction with quiz scores not statistically different from normal playback is a real result, but with 19 vs 21 participants the non-inferiority is underpowered. Profy is promising and appropriately framed as an observed improvement, but ten learners and five raters is preliminary.

I would be happy to see this go to peer review. It would benefit from significance tests, an independent evaluator for AIxSpeed, and ideally released code or data. The author has done the work; the claims just need to be narrowed to what is actually supported. For a reading group, it is a good case study in how honest limitation sections can point the way to the next experiments.

Referee Report

4 major / 6 minor

Summary. The dissertation proposes an AI-guided learning framework organized around three stages (Consume, Understand, Imitate) and instantiates it in three systems: AIxSpeed, which adapts audio playback speed at the phoneme level using speech-recognition confidence as a proxy for listening difficulty; FastPerson, which creates chapter-based video summaries preserving both visual and spoken information and uses voice cloning for continuity; and Profy, which provides pronunciation feedback from largely unannotated audio by highlighting classifier-emphasized waveform regions and latent-space distances. Reported evaluations include: AIxSpeed achieving average playback factors of 1.30x (LibriSpeech) and 1.29x (UME-ERJ) with higher mean opinion scores than matched constant-speed playback; FastPerson reducing viewing time by 53% with no statistically significant quiz-score difference; and Profy showing observed intelligibility improvements with non-overlapping pre/post confidence intervals.

Significance. If the central claims held, the work would make a useful contribution to human-computer interaction and educational technology: it demonstrates concrete, deployable prototypes that address real bottlenecks in audio-visual learning, and it integrates self-supervised speech representations, LLM-based multimodal summarization, and low-resource pronunciation feedback. The manuscript is unusually transparent: it repeatedly states where evidence is missing, e.g., that the AIxSpeed MOS differences were not significance-tested, that the UME-ERJ result does not establish human intelligibility, and that Profy's highlighted regions were not independently validated as phonetic error diagnoses. That transparency is a strength, but it also means the abstract and chapter-level summaries frequently assert more than the evidence supports. The framework itself (Consume-Understand-Imitate) is a reasonable organizing device, and each prototype addresses a genuine gap, so the work has clear value as a systems/HCI contribution if the claims are appropriately qualified.

major comments (4)

[§3.5.1 and §3.3.2] The technical evaluation of AIxSpeed is partly self-referential. The playback speed adjuster is trained jointly with the speech recognizer through the composite loss Loss = Lossspeed + λLossctc (§3.3.2), and the CER/WER evaluation in §3.5.1 uses "the speech recognizer used in our method" to score the modified audio. The lower CER/WER relative to constant-speed playback therefore reflects, at least in part, that the speed profile was optimized to be recognizable to this exact evaluator. The pilot survey in §3.2 validates only aggregate, whole-utterance correlation at constant speeds; it does not validate per-phoneme confidence as a proxy for human intelligibility on non-native or time-stretched speech. The claim that intelligibility is preserved requires either an independent recognizer of a different architecture or a direct human transcription test at phoneme/segment granularity.
[§3.5.2] The higher mean opinion scores for AIxSpeed over matched constant-speed playback are reported without a significance test; the text itself states that "The source paper did not report a significance test for these differences." A 0.5-point and 0.8-point difference on a five-point MOS scale is not interpretable without a test or confidence interval, especially with 50 participants and 40 sentences. Additionally, MOS is a measure of perceived quality, not comprehension, so this result cannot by itself support the abstract's implication that listening comprehension is maintained.
[§4.4.3] The FastPerson quiz result is internally consistent in reporting no statistically significant difference, but the conclusion that comprehension was retained is stronger than the evidence warrants. The comparison is between 19 and 21 participants, and the quiz-score standard deviations are large (e.g., Video 2: 0.67 ± 0.46 for FastPerson vs. 0.63 ± 0.39 for control). A non-significant t-test with these sample sizes does not establish equivalence; a non-inferiority test or a confidence interval for the quiz-score difference is needed. The viewing-time reduction (53%) is well supported by the reported t-tests, so the efficiency claim is robust; the claim of preserved understanding is not established at the same evidentiary level.
[§5.4 and §5.5.2] The Profy evaluation involves only 10 learners and 5 raters, and the headline finding is presented as non-overlapping pre- and post-practice confidence intervals without reporting the interval values or a test statistic. The observed intelligibility gain could reflect practice effects or rater drift, and the elicited-imitation baseline is not accompanied by effect sizes. Furthermore, as the manuscript itself acknowledges in §2.8.2 and §5.5.2, the assumption that classifier-emphasized waveform regions and latent distances correspond to correctable pronunciation deviations was not independently validated. The chapter's central claim of improved pronunciation intelligibility is therefore plausible but not yet firmly supported; reporting raw scores, CIs, and an effect size would help, as would a control condition for the feedback mechanism itself.

minor comments (6)

[§3.2] The formulas for WER and CER state the denominator as "number of correct words" (and characters); standard definitions divide by the total number of reference words/characters. As written, the metric is non-standard and should be clarified or corrected.
[Table 3.1] The CER/WER comparisons in Table 3.1 report point estimates only; confidence intervals or significance tests for the differences would improve interpretability.
[Abstract and §1.7.2] The abstract and the contribution list state that AIxSpeed "received higher mean opinion scores" and that UME-ERJ "suggests" improved listenability for non-native speech. Given the explicit caveats in §3.5.2 and §3.6.4, these statements should be qualified as observed differences without significance testing.
[§4.4.3 and Figure 4.3] The text refers to the "right side" and "left side" of Figure 4.3, but the figure appears as a single panel of per-question correct-answer rates. Please align the description with the actual figure layout.
[§4.2.4] The summary-length weights ws = 0.3 and wv = 2.5 are described as determined by preliminary experiments; reporting the criterion used (e.g., which pilot data, what target) would improve reproducibility.
[§5.4] For the Profy result, please report the actual confidence interval values rather than only stating that they do not overlap, and specify the statistical procedure used to construct them.

Circularity Check

1 steps flagged · score 6.0 of 10

AIxSpeed's ASR-intelligibility result is circular: the speed adjuster is trained against the same recognizer that is then used to evaluate it, leaving only an un-significance-tested MOS comparison as external support.

fitted input called prediction [Section 3.3.2 (Speech Recognizer), Section 3.4.1 (Implementation Details), Section 3.5.1 (Technical Evaluation)]
"In summary, the entire model is trained to minimize the following error function, which is a combination of this error function and the error function of the playback speed adjuster: Loss = Lossspeed + λLossctc. ... A standard Wav2Vec2-based speech recognition model, which was the speech recognizer used in our method, was used to compute the CER and WER for comparison. ... This indicates that it was more recognizable to the ASR model used for evaluation; it does not directly establish improved intelligibility or comprehension for human listeners, which requires a dedicated human study."

The playback-speed adjuster is trained with a joint objective that includes the recognizer's CTC loss (Loss = Lossspeed + λLossctc), and the recognizer's weights are held fixed while the adjuster is optimized against that recognizer. The technical evaluation then measures CER/WER with 'the speech recognizer used in our method'—the exact model whose loss was part of the training objective. Lower CER/WER for AIxSpeed than for matched constant-speed playback therefore partly reflects that the adjuster was fitted to make this recognizer succeed, not an independent measure of human intelligibility.

full rationale

The clearest circularity is localized to AIxSpeed. The speed adjuster is optimized with a loss that explicitly includes the speech recognizer's CTC loss, and the technical evaluation uses that same recognizer to compute CER/WER; the observed improvement over matched constant-speed playback is thus partly a self-consistency check of the training objective rather than an independent intelligibility result. The paper is transparent about this limitation in the quoted passages, but the chapter conclusion still leans on the ASR numbers to claim intelligibility preservation. The pilot survey correlating human and ASR transcription accuracy (r=0.9977) is independent external evidence, though it validates only aggregate speed conditions and not per-phoneme decisions. FastPerson's viewing-time reduction is a measured outcome rather than a fitted prediction, and its quiz scores are independent human outcomes; Profy is evaluated by external human raters. There is no load-bearing self-citation chain. Overall, one central technical result reduces by construction to its training objective, warranting a partial-circularity score of 6.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

The central claims rest on several domain assumptions: ASR confidence standing in for human listening difficulty, LLM summaries preserving pedagogical content, voice cloning preserving learning continuity, and classifier-localized regions corresponding to pronunciation errors. The free parameters listed are hand-set or fitted in preliminary experiments. No new physical entities are introduced.

free parameters (4)

AIxSpeed loss weight lambda = 1e-7
Balances speed maximization and CTC recognition in the combined loss; fixed by hand and not tuned on an independent validation set.
FastPerson summary length weights ws and wv = ws=0.3, wv=2.5
Used in N = max(Nmin, ws*Lt + wv*(Lo+Lc)) to set summary length; calibrated to pilot lecture videos and not cross-validated.
FastPerson segmentation thresholds = Bhattacharyya 0.6; silence RMS below 5% for >1s
Scene and silence detection thresholds empirically determined in preliminary experiments.
FastPerson minimum summary length Nmin = 50 words
Chosen to avoid over-terse summaries; no independent justification is given.

assumptions (6)

domain assumption ASR confidence is a valid proxy for human listening difficulty for the purpose of setting playback speed.
Pilot shows aggregate correlation 0.9977 for speeds above 1.0x, but the system applies it at phoneme level and to non-native speech without per-phoneme human validation (Sections 3.2, 3.3).
domain assumption The aggregate human-ASR correlation extrapolates to fine-grained per-phoneme speed decisions.
The pilot compared whole-sentence transcription at constant speeds; AIxSpeed varies speed by phoneme, a regime not directly validated against human listening (Section 3.3).
domain assumption LLM-based summaries from transcripts, OCR, and object labels retain the content needed for quiz performance.
FastPerson relies on GPT-3.5 to distill multimodal inputs; summary quality is not independently evaluated except through the final quiz (Section 4.2.3).
domain assumption Voice cloning preserves sufficient speaker identity and prosody for seamless learning continuity.
VITS speaker adaptation is used to generate narration; no direct perception test of voice similarity is reported (Section 4.2.5).
ad hoc to paper Classifier-emphasized waveform regions and latent distances correspond to correctable pronunciation deviations.
Profy's feedback assumes model attention localizes errors; the dissertation explicitly notes this was not independently validated as phonetic error diagnosis (Section 2.8.2).
domain assumption Absence of a statistically significant quiz-score difference in a small sample is treated as evidence that summarized viewing does not harm learning.
FastPerson compared 19 vs 21 participants; the study may be underpowered for a non-inferiority conclusion (Section 4.4.3).

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Cite this review

Pith. "Pith review of AI-Guided Learning: Research on Knowledge and Skill Acquisition Support Methods Using Deep Learning Audio-Video Processing Techniques." pith.science (2026). https://pith.science/paper/56IP7E6O

@misc{pith2026260808990,
  author       = {Pith},
  title        = {Pith review of: AI-Guided Learning: Research on Knowledge and Skill Acquisition Support Methods Using Deep Learning Audio-Video Processing Techniques},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/56IP7E6O}},
  note         = {Machine review of arXiv:2608.08990}
}

read the original abstract

Audio and video have become major learning media, but learners face two persistent challenges: the time cost of consuming long-form content sequentially and the lack of scalable feedback for imitation-based skill acquisition. This dissertation proposes an AI-guided learning framework that supports three interconnected stages: Consume, Understand, and Imitate. It develops and evaluates three systems. AIxSpeed dynamically adjusts audio playback speed at the phoneme level using speech-recognition-model confidence as a proxy for listening difficulty. FastPerson generates multimodal video summaries that preserve visual and auditory information and lets learners switch between summarized and full versions by chapter. Profy learns proficiency from largely unannotated speech data and visualizes classifier-relevant regions and model-derived acoustic distances to support pronunciation practice. Technical and user evaluations show that AIxSpeed achieved average playback factors of 1.30x on LibriSpeech and 1.29x on UME-ERJ and received higher mean opinion scores than matched constant-speed playback; FastPerson reduced viewing time by 53% with no statistically significant difference in quiz scores compared with normal playback; and Profy showed an observed improvement in pronunciation intelligibility, with non-overlapping pre- and post-practice confidence intervals. Together, these systems demonstrate how deep learning can support efficient content consumption, multimodal understanding, and repeated skill practice while retaining learner access to the original material.

Figures

Figures reproduced from arXiv: 2608.08990 by the authors.

**Figure 1.1.** An example of a Massive Open Online Course (MOOC) interface, typically [PITH_FULL_IMAGE:figures/full_fig_p012_1_1.png] view at source ↗

**Figure 1.2.** Information-density variations inferred from collective click-frequency traces [PITH_FULL_IMAGE:figures/full_fig_p013_1_2.png] view at source ↗

**Figure 1.3.** An example of imitation learning in an online dance class. Learners attempt [PITH_FULL_IMAGE:figures/full_fig_p013_1_3.png] view at source ↗

Figures from the paper (23 more)

**Figure 1.4.** Figure 1.4: The iterative Consume‒Understand‒Imitate learning loop. Each stage is later augmented by a dedicated AI subsystem (AIxSpeed, FastPerson, and Profy; see §1.5.4). Furthermore, in asynchronous online learning environments, feedback delay is also an important issue. Lear…

**Figure 2.1.** Figure 2.1: A PLATO terminal in use (c. 1972). The networked PLATO system fea [PITH_FULL_IMAGE:figures/full_fig_p023_2_1.png]

**Figure 2.2.** Figure 2.2: The dynamic timeline approach decouples video speed from playback speed, [PITH_FULL_IMAGE:figures/full_fig_p025_2_2.png]

**Figure 2.3.** Figure 2.3: Dynamic skim generation using hierarchical content analysis. The system [PITH_FULL_IMAGE:figures/full_fig_p026_2_3.png]

**Figure 3.1.** Figure 3.1: AIxSpeed optimizes the playback speed of a video in units as small as [PITH_FULL_IMAGE:figures/full_fig_p041_3_1.png]

**Figure 3.2.** Figure 3.2: Comparison of playback speed and listening comprehension in machine [PITH_FULL_IMAGE:figures/full_fig_p043_3_2.png]

**Figure 3.3.** Figure 3.3: The work process of AIxSpeed: extracting human voices from the target [PITH_FULL_IMAGE:figures/full_fig_p045_3_3.png]

**Figure 3.4.** Figure 3.4: AIxSpeed architecture: Our system simultaneously optimizes the playback [PITH_FULL_IMAGE:figures/full_fig_p046_3_4.png]

**Figure 3.5.** Figure 3.5: Application example showing how the playback speed changed when [PITH_FULL_IMAGE:figures/full_fig_p047_3_5.png]

**Figure 3.6.** Figure 3.6: Voice quality comparison between baseline (constant playback speed increase [PITH_FULL_IMAGE:figures/full_fig_p051_3_6.png]

**Figure 4.1.** Figure 4.1: FastPerson: Video summarization method that generates a summary sentence [PITH_FULL_IMAGE:figures/full_fig_p058_4_1.png]

**Figure 4.2.** Figure 4.2: Overview of the FastPerson user interface: A Video Window showcases [PITH_FULL_IMAGE:figures/full_fig_p062_4_2.png]

**Figure 4.3.** Figure 4.3: Correct answer rates for each question of the video. [PITH_FULL_IMAGE:figures/full_fig_p065_4_3.png]

**Figure 4.4.** Figure 4.4: Average video viewing time by viewing method. [PITH_FULL_IMAGE:figures/full_fig_p066_4_4.png]

**Figure 4.5.** Figure 4.5: Survey results on user experience. Q4: What improvements would you suggest for the user interface design? (Openended Response) 2. Learning Experience Satisfaction Q5: Was learning with our system enjoyable? (1: Not Enjoyable At All - 5: Extremely Enjoyable) Q6: Com…

**Figure 5.1.** Figure 5.1: Profy architecture: Overview of the system’s components and data flow. [PITH_FULL_IMAGE:figures/full_fig_p076_5_1.png]

**Figure 5.2.** Figure 5.2: Profy pronunciation score determination: Process of classifying speech data [PITH_FULL_IMAGE:figures/full_fig_p077_5_2.png]

**Figure 5.3.** Figure 5.3: Profy difference visualization: Method for highlighting parts of speech that [PITH_FULL_IMAGE:figures/full_fig_p080_5_3.png]

**Figure 5.4.** Figure 5.4: Profy distance visualization: Representation of speech data in two [PITH_FULL_IMAGE:figures/full_fig_p081_5_4.png]

**Figure 5.5.** Figure 5.5: Profy user interface: Layout and components of the application’s main screen. [PITH_FULL_IMAGE:figures/full_fig_p082_5_5.png]

**Figure 5.6.** Figure 5.6: Examples of Profy interactions: Demonstration of eliminating differences [PITH_FULL_IMAGE:figures/full_fig_p082_5_6.png]

**Figure 5.7.** Figure 5.7: Comparison of post-practice intelligibility gains for users of Profy and stan [PITH_FULL_IMAGE:figures/full_fig_p084_5_7.png]

**Figure 5.8.** Figure 5.8: Questionnaire results: User feedback on Profy’s ease of use and helpfulness [PITH_FULL_IMAGE:figures/full_fig_p084_5_8.png]

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Reviewed August 14, 2026 · model on record in the stance chip above.

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Lorin W. Anderson and David R. Krathwohl, editors. A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objec- tives. Longman, New Y ork, 2001

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Evlampios Apostolidis, Eleni Adamantidou, Alexandros I. Metsai, Vasileios Mezaris, and Ioannis Patras. Video summarization using deep neural networks: A survey. Proceedings of the IEEE , 109(11):1838–1863, 2021

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Rosana Ardila, Megan Branson, Kelly Davis, Michael Kohler, Josh Meyer, Michael Henretty, Reuben Morais, Lindsay Saunders, Francis T yers, and Gregor Weber. Common voice: A massively-multilingual speech corpus. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 4218–4222. European Language Resources Association, 2020

2020