Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Ronan Collobert"'
Publikováno v:
PLoS ONE, Vol 4, Iss 7, p e6393 (2009)
To reduce the increasing amount of time spent on literature search in the life sciences, several methods for automated knowledge extraction have been developed. Co-occurrence based approaches can deal with large text corpora like MEDLINE in an accept
Externí odkaz:
https://doaj.org/article/e00ebc70e9c548a597ec3f637edeff70
In this paper, we study training of automatic speech recognition system in a weakly supervised setting where the order of words in transcript labels of the audio training data is not known. We train a word-level acoustic model which aggregates the di
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::33f3599023480c64f7a523b9cb6b2442
http://arxiv.org/abs/2110.05994
http://arxiv.org/abs/2110.05994
Autor:
Vineel Pratap, Michael Auli, Ann B. Lee, Tatiana Likhomanenko, Gabriel Synnaeve, Alexei Baevski, Ronan Collobert, Anuroop Sriram, Qiantong Xu, Jacob Kahn, Wei-Ning Hsu
Publikováno v:
Interspeech 2021.
Self-supervised learning of speech representations has been a very active research area but most work is focused on a single domain such as read audio books for which there exist large quantities of labeled and unlabeled data. In this paper, we explo
Autor:
Michael Auli, Alexis Conneau, Tatiana Likhomanenko, Qiantong Xu, Paden Tomasello, Gabriel Synnaeve, Alexei Baevski, Ronan Collobert
Publikováno v:
ICASSP
Self-training and unsupervised pre-training have emerged as effective approaches to improve speech recognition systems using unlabeled data. However, it is not clear whether they learn similar patterns or if they can be effectively combined. In this
Publikováno v:
ICASSP
Self-supervised learning (SSL) has shown promise in learning representations of audio that are useful for automatic speech recognition (ASR). But, training SSL models like wav2vec~2.0 requires a two-stage pipeline. In this paper we demonstrate a sing
Publikováno v:
Speech Communication. 108:15-32
In hidden Markov model (HMM) based automatic speech recognition (ASR) system, modeling the statistical relationship between the acoustic speech signal and the HMM states that represent linguistically motivated subword units such as phonemes is a cruc
Autor:
Jacob Kahn, Gabriel Synnaeve, Tatiana Likhomanenko, Qiantong Xu, Ronan Collobert, Awni Hannun
Publikováno v:
INTERSPEECH
Pseudo-labeling has recently shown promise in end-to-end automatic speech recognition (ASR). We study Iterative Pseudo-Labeling (IPL), a semi-supervised algorithm which efficiently performs multiple iterations of pseudo-labeling on unlabeled data as
Publikováno v:
INTERSPEECH
This paper introduces Multilingual LibriSpeech (MLS) dataset, a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages, including about 44.5K hours of English and
Recent results in end-to-end automatic speech recognition have demonstrated the efficacy of pseudo-labeling for semi-supervised models trained both with Connectionist Temporal Classification (CTC) and Sequence-to-Sequence (seq2seq) losses. Iterative
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e81380b3c44b677c1551436c9a32d51b
http://arxiv.org/abs/2010.11524
http://arxiv.org/abs/2010.11524
Autor:
Ronan Collobert, Gabriel Synnaeve, Paden Tomasello, Vitaliy Liptchinsky, Awni Hannun, Vineel Pratap, Anuroop Sriram
Publikováno v:
INTERSPEECH
We study training a single acoustic model for multiple languages with the aim of improving automatic speech recognition (ASR) performance on low-resource languages, and over-all simplifying deployment of ASR systems that support diverse languages. We
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ef82d1005bea91eee550ee2ee3cd9637
http://arxiv.org/abs/2007.03001
http://arxiv.org/abs/2007.03001