1 code implementation • 31 Jan 2023 • Katerina Zmolikova, Marc Delcroix, Tsubasa Ochiai, Keisuke Kinoshita, Jan Černocký, Dong Yu
Humans can listen to a target speaker even in challenging acoustic conditions that have noise, reverberation, and interfering speakers.
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.
3 code implementations • 8 Nov 2022 • Juan Zuluaga-Gomez, Karel Veselý, Igor Szöke, Alexander Blatt, Petr Motlicek, Martin Kocour, Mickael Rigault, Khalid Choukri, Amrutha Prasad, Seyyed Saeed Sarfjoo, Iuliia Nigmatulina, Claudia Cevenini, Pavel Kolčárek, Allan Tart, Jan Černocký, Dietrich Klakow
In this paper, we introduce the ATCO2 corpus, a dataset that aims at fostering research on the challenging ATC field, which has lagged behind due to lack of annotated data.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +6
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.
1 code implementation • 11 Nov 2021 • Ladislav Mošner, Oldřich Plchot, Lukáš Burget, Jan Černocký
Motivated by unconsolidated data situation and the lack of a standard benchmark in the field, we complement our previous efforts and present a comprehensive corpus designed for training and evaluating text-independent multi-channel speaker verification systems.
1 code implementation • 15 Aug 2022 • Ján Švec, Kateřina Žmolíková, Martin Kocour, Marc Delcroix, Tsubasa Ochiai, Ladislav Mošner, Jan Černocký
One of the factors causing such degradation may be intrinsic speaker variability, such as emotions, occurring commonly in realistic speech.
no code implementations • 30 Apr 2019 • Murali Karthick Baskar, Shinji Watanabe, Ramon Astudillo, Takaaki Hori, Lukáš Burget, Jan Černocký
Such techniques derive training procedures and losses able to leverage unpaired speech and/or text data by combining ASR with Text-to-Speech (TTS) models.
Ranked #33 on Semi-Supervised Image Classification on ImageNet - 10% labeled data (Top 5 Accuracy metric)
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • LEGAL (LREC) 2022 • Mickaël Rigault, Claudia Cevenini, Khalid Choukri, Martin Kocour, Karel Veselý, Igor Szoke, Petr Motlicek, Juan Pablo Zuluaga-Gomez, Alexander Blatt, Dietrich Klakow, Allan Tart, Pavel Kolčárek, Jan Černocký
In this paper the authors detail the various legal and ethical issues faced during the ATCO2 project.
no code implementations • 28 Oct 2022 • Junyi Peng, Themos Stafylakis, Rongzhi Gu, Oldřich Plchot, Ladislav Mošner, Lukáš Burget, Jan Černocký
Recently, the pre-trained Transformer models have received a rising interest in the field of speech processing thanks to their great success in various downstream tasks.
no code implementations • 17 May 2023 • Junyi Peng, Oldřich Plchot, Themos Stafylakis, Ladislav Mošner, Lukáš Burget, Jan Černocký
Recently, fine-tuning large pre-trained Transformer models using downstream datasets has received a rising interest.