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pro vyhledávání: '"de Seyssel, Maureen"'
We introduce EmphAssess, a prosodic benchmark designed to evaluate the capability of speech-to-speech models to encode and reproduce prosodic emphasis. We apply this to two tasks: speech resynthesis and speech-to-speech translation. In both cases, th
Externí odkaz:
http://arxiv.org/abs/2312.14069
Autor:
de Seyssel, Maureen, Lavechin, Marvin, Titeux, Hadrien, Thomas, Arthur, Virlet, Gwendal, Revilla, Andrea Santos, Wisniewski, Guillaume, Ludusan, Bogdan, Dupoux, Emmanuel
We present ProsAudit, a benchmark in English to assess structural prosodic knowledge in self-supervised learning (SSL) speech models. It consists of two subtasks, their corresponding metrics, and an evaluation dataset. In the protosyntax task, the mo
Externí odkaz:
http://arxiv.org/abs/2302.12057
Autor:
Nguyen, Tu Anh, de Seyssel, Maureen, Algayres, Robin, Roze, Patricia, Dunbar, Ewan, Dupoux, Emmanuel
Word or word-fragment based Language Models (LM) are typically preferred over character-based ones in many downstream applications. This may not be surprising as words seem more linguistically relevant units than characters. Words provide at least tw
Externí odkaz:
http://arxiv.org/abs/2210.02956
According to the Language Familiarity Effect (LFE), people are better at discriminating between speakers of their native language. Although this cognitive effect was largely studied in the literature, experiments have only been conducted on a limited
Externí odkaz:
http://arxiv.org/abs/2206.13415
Unsupervised models of representations based on Contrastive Predictive Coding (CPC)[1] are primarily used in spoken language modelling in that they encode phonetic information. In this study, we ask what other types of information are present in CPC
Externí odkaz:
http://arxiv.org/abs/2203.16193
Autor:
Lavechin, Marvin, de Seyssel, Maureen, Métais, Marianne, Metze, Florian, Mohamed, Abdelrahman, Bredin, Hervé, Dupoux, Emmanuel, Cristia, Alejandrina
Publikováno v:
In Cognition April 2024 245
Autor:
Dunbar, Ewan, Bernard, Mathieu, Hamilakis, Nicolas, Nguyen, Tu Anh, de Seyssel, Maureen, Rozé, Patricia, Rivière, Morgane, Kharitonov, Eugene, Dupoux, Emmanuel
We present the Zero Resource Speech Challenge 2021, which asks participants to learn a language model directly from audio, without any text or labels. The challenge is based on the Libri-light dataset, which provides up to 60k hours of audio from Eng
Externí odkaz:
http://arxiv.org/abs/2104.14700
Autor:
Nguyen, Tu Anh, de Seyssel, Maureen, Rozé, Patricia, Rivière, Morgane, Kharitonov, Evgeny, Baevski, Alexei, Dunbar, Ewan, Dupoux, Emmanuel
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 probin
Externí odkaz:
http://arxiv.org/abs/2011.11588
Publikováno v:
DEFT 2019
This paper reports on Qwant Research contribution to tasks 2 and 3 of the DEFT 2019's challenge, focusing on French clinical cases analysis. Task 2 is a task on semantic similarity between clinical cases and discussions. For this task, we propose an
Externí odkaz:
http://arxiv.org/abs/1907.05790
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