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The Zero Resource Speech Challenge 2021: Spoken language modelling
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In: ISSN: 0162-8828 ; IEEE Transactions on Pattern Analysis and Machine Intelligence ; https://hal.inria.fr/hal-03329301 ; IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2021, pp.1-1. ⟨10.1109/TPAMI.2021.3083839⟩ (2021)
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The Zero Resource Speech Challenge 2021: Spoken language modelling
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In: Interspeech 2021 - Conference of the International Speech Communication Association ; https://hal.inria.fr/hal-03329301 ; Interspeech 2021 - Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic. ⟨10.1109/TPAMI.2021.3083839⟩ (2021)
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VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation
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In: https://hal.inria.fr/hal-03329290 ; 2021, ⟨10.18653/v1/2021.acl-long.80⟩ (2021)
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VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation ...
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VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation ...
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The Zero Resource Speech Challenge 2021: Spoken language modelling ...
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Textless Speech Emotion Conversion using Discrete and Decomposed Representations ...
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Kreuk, Felix; Polyak, Adam; Copet, Jade; Kharitonov, Eugene; Nguyen, Tu-Anh; Rivière, Morgane; Hsu, Wei-Ning; Mohamed, Abdelrahman; Dupoux, Emmanuel; Adi, Yossi. - : arXiv, 2021
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Abstract:
Speech emotion conversion is the task of modifying the perceived emotion of a speech utterance while preserving the lexical content and speaker identity. In this study, we cast the problem of emotion conversion as a spoken language translation task. We use a decomposition of the speech signal into discrete learned representations, consisting of phonetic-content units, prosodic features, speaker, and emotion. First, we modify the speech content by translating the phonetic-content units to a target emotion, and then predict the prosodic features based on these units. Finally, the speech waveform is generated by feeding the predicted representations into a neural vocoder. Such a paradigm allows us to go beyond spectral and parametric changes of the signal, and model non-verbal vocalizations, such as laughter insertion, yawning removal, etc. We demonstrate objectively and subjectively that the proposed method is vastly superior to current approaches and even beats text-based systems in terms of perceived emotion ...
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Keyword:
Artificial Intelligence cs.AI; Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Machine Learning cs.LG; Sound cs.SD
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URL: https://arxiv.org/abs/2111.07402 https://dx.doi.org/10.48550/arxiv.2111.07402
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The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling
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In: NeuRIPS Workshop on Self-Supervised Learning for Speech and Audio Processing ; https://hal.archives-ouvertes.fr/hal-03070362 ; NeuRIPS Workshop on Self-Supervised Learning for Speech and Audio Processing, Dec 2020, Virtuel, France (2020)
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Towards unsupervised learning of speech features in the wild
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In: SLT 2020 : IEEE Spoken Language Technology Workshop ; https://hal.archives-ouvertes.fr/hal-03070411 ; SLT 2020 : IEEE Spoken Language Technology Workshop, Dec 2020, Shenzhen / Virtual, China (2020)
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LIBRI-LIGHT: a benchmark for asr with limited or no supervision
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In: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing ; https://hal.archives-ouvertes.fr/hal-02959460 ; ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, May 2020, Barcelona / Virtual, Spain. pp.7669-7673, ⟨10.1109/ICASSP40776.2020.9052942⟩ (2020)
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Data Augmenting Contrastive Learning of Speech Representations in the Time Domain
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In: SLT 2020 - IEEE Spoken Language Technology Workshop ; https://hal.archives-ouvertes.fr/hal-03070321 ; SLT 2020 - IEEE Spoken Language Technology Workshop, Dec 2020, Shenzhen / Virtual, China (2020)
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Unsupervised pretraining transfers well across languages
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In: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing ; https://hal.archives-ouvertes.fr/hal-02959418 ; ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, May 2020, Barcelona / Virtual, Spain. pp.7414-7418, ⟨10.1109/ICASSP40776.2020.9054548⟩ (2020)
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Unsupervised pretraining transfers well across languages ...
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The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling ...
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