1 code implementation • 31 Oct 2021 • Martin Kocour, Kateřina Žmolíková, Lucas Ondel, Ján Švec, Marc Delcroix, Tsubasa Ochiai, Lukáš Burget, Jan Černocký
We modify the acoustic model to predict joint state posteriors for all speakers, enabling the network to express uncertainty about the attribution of parts of the speech signal to the speakers.
no code implementations • 22 Oct 2021 • Lucas Ondel, Léa-Marie Lam-Yee-Mui, Martin Kocour, Caio Filippo Corro, Lukáš Burget
We propose to express the forward-backward algorithm in terms of operations between sparse matrices in a specific semiring.
no code implementations • SIGUL (LREC) 2022 • Marcely Zanon Boito, Bolaji Yusuf, Lucas Ondel, Aline Villavicencio, Laurent Besacier
Our results suggest that neural models for speech discretization are difficult to exploit in our setting, and that it might be necessary to adapt them to limit sequence length.
no code implementations • 4 Nov 2020 • Bolaji Yusuf, Lucas Ondel, Lukas Burget, Jan Cernocky, Murat Saraclar
In the target language, we infer both the language and unit embeddings in an unsupervised manner, and in so doing, we simultaneously learn a subspace of units specific to that language and the units that dwell on it.
no code implementations • 12 Oct 2020 • Ewan Dunbar, Julien Karadayi, Mathieu Bernard, Xuan-Nga Cao, Robin Algayres, Lucas Ondel, Laurent Besacier, Sakriani Sakti, Emmanuel Dupoux
We present the Zero Resource Speech Challenge 2020, which aims at learning speech representations from raw audio signals without any labels.
no code implementations • 19 May 2020 • Bolaji Yusuf, Lucas Ondel
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 quality used as a proxy metric for the unit quality.
no code implementations • 25 Apr 2019 • Ewan Dunbar, Robin Algayres, Julien Karadayi, Mathieu Bernard, Juan Benjumea, Xuan-Nga Cao, Lucie Miskic, Charlotte Dugrain, Lucas Ondel, Alan W. black, Laurent Besacier, Sakriani Sakti, Emmanuel Dupoux
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).
1 code implementation • 8 Apr 2019 • Lucas Ondel, Hari Krishna Vydana, Lukáš Burget, Jan Černocký
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.
no code implementations • 18 Jun 2018 • Pierre Godard, Marcely Zanon-Boito, Lucas Ondel, Alexandre Berard, François Yvon, Aline Villavicencio, Laurent Besacier
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).
no code implementations • 23 Feb 2018 • Matthew Wiesner, Chunxi Liu, Lucas Ondel, Craig Harman, Vimal Manohar, Jan Trmal, Zhongqiang Huang, Najim Dehak, Sanjeev Khudanpur
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
no code implementations • 16 Feb 2018 • Lucas Ondel, Pierre Godard, Laurent Besacier, Elin Larsen, Mark Hasegawa-Johnson, Odette Scharenborg, Emmanuel Dupoux, Lukas Burget, François Yvon, Sanjeev Khudanpur
Developing speech technologies for low-resource languages has become a very active research field over the last decade.
no code implementations • 14 Feb 2018 • Odette Scharenborg, Laurent Besacier, Alan Black, Mark Hasegawa-Johnson, Florian Metze, Graham Neubig, Sebastian Stueker, Pierre Godard, Markus Mueller, Lucas Ondel, Shruti Palaskar, Philip Arthur, Francesco Ciannella, Mingxing Du, Elin Larsen, Danny Merkx, Rachid Riad, Liming Wang, Emmanuel Dupoux
We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding the discovery of linguistic units (subwords and words) in a language without orthography.
no code implementations • 5 Feb 2017 • Chunxi Liu, Jinyi Yang, Ming Sun, Santosh Kesiraju, Alena Rott, Lucas Ondel, Pegah Ghahremani, Najim Dehak, Lukas Burget, Sanjeev Khudanpur
Acoustic unit discovery (AUD) is a process of automatically identifying a categorical acoustic unit inventory from speech and producing corresponding acoustic unit tokenizations.