Search Results for author: Tu Anh Nguyen

Found 12 papers, 3 papers with code

EXPRESSO: A Benchmark and Analysis of Discrete Expressive Speech Resynthesis

no code implementations10 Aug 2023 Tu Anh Nguyen, Wei-Ning Hsu, Antony D'Avirro, Bowen Shi, Itai Gat, Maryam Fazel-Zarani, Tal Remez, Jade Copet, Gabriel Synnaeve, Michael Hassid, Felix Kreuk, Yossi Adi, Emmanuel Dupoux

Recent work has shown that it is possible to resynthesize high-quality speech based, not on text, but on low bitrate discrete units that have been learned in a self-supervised fashion and can therefore capture expressive aspects of speech that are hard to transcribe (prosody, voice styles, non-verbal vocalization).

Resynthesis Speech Synthesis

Are word boundaries useful for unsupervised language learning?

no code implementations6 Oct 2022 Tu Anh Nguyen, Maureen de Seyssel, Robin Algayres, Patricia Roze, Ewan Dunbar, Emmanuel Dupoux

However, word boundary information may be absent or unreliable in the case of speech input (word boundaries are not marked explicitly in the speech stream).

Are discrete units necessary for Spoken Language Modeling?

no code implementations11 Mar 2022 Tu Anh Nguyen, Benoit Sagot, Emmanuel Dupoux

The approach relies first on transforming the audio into a sequence of discrete units (or pseudo-text) and then training a language model directly on such pseudo-text.

Language Modelling

textless-lib: a Library for Textless Spoken Language Processing

1 code implementation NAACL (ACL) 2022 Eugene Kharitonov, Jade Copet, Kushal Lakhotia, Tu Anh Nguyen, Paden Tomasello, Ann Lee, Ali Elkahky, Wei-Ning Hsu, Abdelrahman Mohamed, Emmanuel Dupoux, Yossi Adi

Textless spoken language processing research aims to extend the applicability of standard NLP toolset onto spoken language and languages with few or no textual resources.

Resynthesis

The Zero Resource Speech Challenge 2021: Spoken language modelling

no code implementations29 Apr 2021 Ewan Dunbar, Mathieu Bernard, Nicolas Hamilakis, Tu Anh Nguyen, Maureen de Seyssel, Patricia Rozé, Morgane Rivière, Eugene Kharitonov, Emmanuel Dupoux

We present the Zero Resource Speech Challenge 2021, which asks participants to learn a language model directly from audio, without any text or labels.

Language Modelling

The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling

2 code implementations23 Nov 2020 Tu Anh Nguyen, Maureen de Seyssel, Patricia Rozé, Morgane Rivière, Evgeny Kharitonov, Alexei Baevski, Ewan Dunbar, Emmanuel Dupoux

We introduce a new unsupervised task, spoken language modeling: the learning of linguistic representations from raw audio signals without any labels, along with the Zero Resource Speech Benchmark 2021: a suite of 4 black-box, zero-shot metrics probing for the quality of the learned models at 4 linguistic levels: phonetics, lexicon, syntax and semantics.

Clustering Language Modelling +1

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