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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 Speech Resynthesis from Discrete Disentangled Self-Supervised Representations Polyak, Adam; Adi, Yossi; Copet, Jade... In: INTERSPEECH 2021 - Annual Conference of the International Speech Communication Association ; https://hal.inria.fr/hal-03329245 ; INTERSPEECH 2021 - Annual Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic (2021) BASE Show details 2 On Generative Spoken Language Modeling from Raw Audio Lakhotia, Kushal; Kharitonov, Evgeny; Hsu, Wei-Ning... In: EISSN: 2307-387X ; Transactions of the Association for Computational Linguistics ; https://hal.inria.fr/hal-03329219 ; Transactions of the Association for Computational Linguistics, The MIT Press, 2021 (2021) BASE Show details 3 Generative Spoken Language Modeling from Raw Audio ... Lakhotia, Kushal; Kharitonov, Evgeny; Hsu, Wei-Ning; Adi, Yossi; Polyak, Adam; Bolte, Benjamin; Nguyen, Tu-Anh; Copet, Jade; Baevski, Alexei; Mohamed, Adelrahman; Dupoux, Emmanuel. - : arXiv, 2021 Abstract: We introduce Generative Spoken Language Modeling, the task of learning the acoustic and linguistic characteristics of a language from raw audio (no text, no labels), and a set of metrics to automatically evaluate the learned representations at acoustic and linguistic levels for both encoding and generation. We set up baseline systems consisting of a discrete speech encoder (returning pseudo-text units), a generative language model (trained on pseudo-text), and a speech decoder (generating a waveform from pseudo-text) all trained without supervision and validate the proposed metrics with human evaluation. Across 3 speech encoders (CPC, wav2vec 2.0, HuBERT), we find that the number of discrete units (50, 100, or 200) matters in a task-dependent and encoder-dependent way, and that some combinations approach text-based systems. ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2102.01192 https://dx.doi.org/10.48550/arxiv.2102.01192 BASE Hide details 4 Generative Spoken Language Modeling from Raw Audio ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Adi, Yossi; Baevski, Alexei. - : Underline Science Inc., 2021 BASE Show details 5 Textless Speech Emotion Conversion using Discrete and Decomposed Representations ... Kreuk, Felix; Polyak, Adam; Copet, Jade. - : arXiv, 2021 BASE Show details 6 VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop ... Taigman, Yaniv; Wolf, Lior; Polyak, Adam. - : arXiv, 2017 BASE Show details

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