Project Details
Speech enhancement front-end for robust automatic speech recognition with large amount of training data
Project Period: 2. 1. 2023 – 31. 1. 2024
Project Type: contract
Partner: NTT Corporation
Czech title
Parametrizace s obohacováním řeči pro robustní automatické rozpoznávání řeči s velkým objemem trénovacích dat
Type
contract
Keywords
speech recognition, speaker diarization, large data, robustness
Abstract
The joint research will aim at investigating and developing speech enhancement and speaker diarization techniques for automatic speech recognition systems that are trained using a large amount of training data.
Team members
Diez Sánchez Mireia, M.Sc., Ph.D.
(DCGM)
– research leader
Černocký Jan, prof. Dr. Ing. (DCGM)
Pavlus Ján, Ing. (DCGM)
Peng Junyi (DCGM)
Švec Ján, Ing. (DCGM)
Černocký Jan, prof. Dr. Ing. (DCGM)
Pavlus Ján, Ing. (DCGM)
Peng Junyi (DCGM)
Švec Ján, Ing. (DCGM)
Publications
2023
- DELCROIX, M.; TAWARA, N.; DIEZ SÁNCHEZ, M.; LANDINI, F.; SILNOVA, A.; OGAWA, A.; NAKATANI, T.; BURGET, L.; ARAKI, S. Multi-Stream Extension of Variational Bayesian HMM Clustering (MS-VBx) for Combined End-to-End and Vector Clustering-based Diarization. In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH. Proceedings of Interspeech. Dublin: International Speech Communication Association, 2023.
p. 3477-3481. ISSN: 1990-9772. Detail