Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Ondel, Lucas"'
Autor:
Kocour, Martin, Žmolíková, Kateřina, Ondel, Lucas, Švec, Ján, Delcroix, Marc, Ochiai, Tsubasa, Burget, Lukáš, Černocký, Jan
In typical multi-talker speech recognition systems, a neural network-based acoustic model predicts senone state posteriors for each speaker. These are later used by a single-talker decoder which is applied on each speaker-specific output stream separ
Externí odkaz:
http://arxiv.org/abs/2111.00009
We propose to express the forward-backward algorithm in terms of operations between sparse matrices in a specific semiring. This new perspective naturally leads to a GPU-friendly algorithm which is easy to implement in Julia or any programming langua
Externí odkaz:
http://arxiv.org/abs/2112.00709
Documenting languages helps to prevent the extinction of endangered dialects, many of which are otherwise expected to disappear by the end of the century. When documenting oral languages, unsupervised word segmentation (UWS) from speech is a useful,
Externí odkaz:
http://arxiv.org/abs/2106.04298
In this work, we propose a hierarchical subspace model for acoustic unit discovery. In this approach, we frame the task as one of learning embeddings on a low-dimensional phonetic subspace, and simultaneously specify the subspace itself as an embeddi
Externí odkaz:
http://arxiv.org/abs/2011.03115
Autor:
Dunbar, Ewan, Karadayi, Julien, Bernard, Mathieu, Cao, Xuan-Nga, Algayres, Robin, Ondel, Lucas, Besacier, Laurent, Sakti, Sakriani, Dupoux, Emmanuel
Publikováno v:
Proceedings of Interspeech 2020
We present the Zero Resource Speech Challenge 2020, which aims at learning speech representations from raw audio signals without any labels. It combines the data sets and metrics from two previous benchmarks (2017 and 2019) and features two tasks whi
Externí odkaz:
http://arxiv.org/abs/2010.05967
Autor:
Yusuf, Bolaji, Ondel, Lucas
In this paper we describe our submission to the Zerospeech 2020 challenge, where the participants are required to discover latent representations from unannotated speech, and to use those representations to perform speech synthesis, with synthesis qu
Externí odkaz:
http://arxiv.org/abs/2005.09282
Autor:
Dunbar, Ewan, Algayres, Robin, Karadayi, Julien, Bernard, Mathieu, Benjumea, Juan, Cao, Xuan-Nga, Miskic, Lucie, Dugrain, Charlotte, Ondel, Lucas, Black, Alan W., Besacier, Laurent, Sakti, Sakriani, Dupoux, Emmanuel
We present the Zero Resource Speech Challenge 2019, which proposes to build a speech synthesizer without any text or phonetic labels: hence, TTS without T (text-to-speech without text). We provide raw audio for a target voice in an unknown language (
Externí odkaz:
http://arxiv.org/abs/1904.11469
This work tackles the problem of learning a set of language specific acoustic units from unlabeled speech recordings given a set of labeled recordings from other languages. Our approach may be described by the following two steps procedure: first the
Externí odkaz:
http://arxiv.org/abs/1904.03876
Autor:
Godard, Pierre, Zanon-Boito, Marcely, Ondel, Lucas, Berard, Alexandre, Yvon, François, Villavicencio, Aline, Besacier, Laurent
We present a first attempt to perform attentional word segmentation directly from the speech signal, with the final goal to automatically identify lexical units in a low-resource, unwritten language (UL). Our methodology assumes a pairing between rec
Externí odkaz:
http://arxiv.org/abs/1806.06734
Autor:
Wiesner, Matthew, Liu, Chunxi, Ondel, Lucas, Harman, Craig, Manohar, Vimal, Trmal, Jan, Huang, Zhongqiang, Dehak, Najim, Khudanpur, Sanjeev
Automatic speech recognition (ASR) systems often need to be developed for extremely low-resource languages to serve end-uses such as audio content categorization and search. While universal phone recognition is natural to consider when no transcribed
Externí odkaz:
http://arxiv.org/abs/1802.08731