Project Details

Robust SPEAKER DIariazation systems using Bayesian inferenCE and deep learning methods

Project Period: 1. 3. 2017 – 28. 2. 2019

Project Type: grant

Agency: Evropská unie

Program: Horizon 2020

Czech title
Robustní diarizace mluvčích pomocí Bayesovské inference a hlubokého učení
Type
grant
Keywords

Machine learning, statistical data processing and applications using signal processing, Numerical analysis, simulation, optimisation, modelling tools, data mining, Ontologies, neural networks, genetic programming, fuzzy logic, Cognitive science, human computer interaction, natural language processing, Complexity and cryptography, electronic security, privacy, biometrics, Speaker Diarization, Speaker Recognition, Variational Bayes Inference, Deep Neural Networks, Speech Data Mining

Abstract

The proposed project deals with Speaker Diarization (SD) which is commonly defined as the task of answering the question "who spoke when?" in a speech recording. The first objective of the proposal is to optimize the Bayesian approach to SD, which has shown to be promising for the tasks. For Variational Bayes (VB) inference, that is very sensitive to initialization, we will develop new fast ways of obtaining a good starting point. We will also explore alternative inference methods, such as collapsed VB or collapsed Gibbs Sampling, and investigate into alternative priors similar to those introduced for Bayesian speaker recognition models. The second part of the proposal is motivated by the huge performance gains that, in recent years, have been brought to other recognition tasks by Deep Neural Networks (DNNs). In the context of SD, DNNs have been used in the computation of i-vectors, but their potential was never explored for other stages of SD. We will study ways of integrating DNNs in the different stages of SD systems. The objectives of the proposal will be achieved by theoretical work, implementation, and careful testing on real speech data. The outcomes of the project are intended not only for scientific publications, but eagerly awaited by European speech data mining industry (for example Czech Phonexia or Spanish Agnitio). The project is proposed by an excellent female researcher, Dr. Mireia Diez, having finished her thesis in the GTTS group of University of the Basque Country, one of the most important European labs dealing with speaker recognition and diarization. The proposed host is the Speech@FIT group of Brno University of Technology, with a 20-year track of top speech data mining research. The proposed research training and combination of skills of Dr. Diez and the host institution have chances to advance the state-of-the-art in speaker diarization, provide the applicant with improved career opportunities and benefit European industry.

Team members
Diez Sánchez Mireia, M.Sc., Ph.D. (DCGM) – research leader
Mošner Ladislav, Ing. (DCGM)
Support

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This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 748097.

Publications

2020

  • DIEZ SÁNCHEZ, M.; BURGET, L.; LANDINI, F.; ČERNOCKÝ, J. Analysis of Speaker Diarization based on Bayesian HMM with Eigenvoice Priors. IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH AND LANGUAGE PROCESSING, 2020, vol. 28, no. 1, p. 355-368. ISSN: 2329-9290. Detail
  • MATĚJKA, P.; PLCHOT, O.; GLEMBEK, O.; BURGET, L.; ROHDIN, J.; ZEINALI, H.; MOŠNER, L.; SILNOVA, A.; NOVOTNÝ, O.; DIEZ SÁNCHEZ, M.; ČERNOCKÝ, J. 13 years of speaker recognition research at BUT, with longitudinal analysis of NIST SRE. COMPUTER SPEECH AND LANGUAGE, 2020, vol. 2020, no. 63, p. 1-15. ISSN: 0885-2308. Detail

2019

  • DIEZ SÁNCHEZ, M.; BURGET, L.; WANG, S.; ROHDIN, J.; ČERNOCKÝ, J. Bayesian HMM based x-vector clustering for Speaker Diarization. In Proceedings of Interspeech. Proceedings of Interspeech. Graz: International Speech Communication Association, 2019. p. 346-350. ISSN: 1990-9772. Detail
  • MATĚJKA, P.; PLCHOT, O.; ZEINALI, H.; MOŠNER, L.; SILNOVA, A.; BURGET, L.; NOVOTNÝ, O.; GLEMBEK, O. Analysis of BUT Submission in Far-Field Scenarios of VOiCES 2019 Challenge. In Proceedings of Interspeech. Proceedings of Interspeech. Graz: International Speech Communication Association, 2019. p. 2448-2452. ISSN: 1990-9772. Detail

2018

  • DIEZ SÁNCHEZ, M.; BURGET, L.; MATĚJKA, P. Speaker Diarization based on Bayesian HMM with Eigenvoice Priors. In Proceedings of Odyssey 2018. Proceedings of Odyssey: The Speaker and Language Recognition Workshop Odyssey 2014, Joensuu, Finland. Les Sables d´Olonne: International Speech Communication Association, 2018. p. 147-154. ISSN: 2312-2846. Detail
  • DIEZ SÁNCHEZ, M.; LANDINI, F.; BURGET, L.; ROHDIN, J.; SILNOVA, A.; ŽMOLÍKOVÁ, K.; NOVOTNÝ, O.; VESELÝ, K.; GLEMBEK, O.; PLCHOT, O.; MOŠNER, L.; MATĚJKA, P. BUT system for DIHARD Speech Diarization Challenge 2018. In Proceedings of Interspeech 2018. Proceedings of Interspeech. Hyderabad: International Speech Communication Association, 2018. p. 2798-2802. ISSN: 1990-9772. Detail
  • PLCHOT, O.; MATĚJKA, P.; NOVOTNÝ, O.; CUMANI, S.; LOZANO DÍEZ, A.; SLAVÍČEK, J.; DIEZ SÁNCHEZ, M.; GRÉZL, F.; GLEMBEK, O.; KAMSALI VEERA, M.; SILNOVA, A.; BURGET, L.; ONDEL YANG, L.; KESIRAJU, S.; ROHDIN, J. Analysis of BUT-PT Submission for NIST LRE 2017. In Proceedings of Odyssey 2018 The Speaker and Language Recognition Workshop. Proceedings of Odyssey: The Speaker and Language Recognition Workshop Odyssey 2014, Joensuu, Finland. Les Sables d'Olonne: International Speech Communication Association, 2018. p. 47-53. ISSN: 2312-2846. Detail
  • ROHDIN, J.; SILNOVA, A.; DIEZ SÁNCHEZ, M.; PLCHOT, O.; MATĚJKA, P.; BURGET, L. End-to-End DNN Based Speaker Recognition Inspired by i-Vector and PLDA. In Proceedings of ICASSP. Calgary: IEEE Signal Processing Society, 2018. p. 4874-4878. ISBN: 978-1-5386-4658-8. Detail

2017

  • MATĚJKA, P.; NOVOTNÝ, O.; PLCHOT, O.; BURGET, L.; DIEZ SÁNCHEZ, M.; ČERNOCKÝ, J. Analysis of Score Normalization in Multilingual Speaker Recognition. In Proceedings of Interspeech 2017. Proceedings of Interspeech. Stockholm: International Speech Communication Association, 2017. p. 1567-1571. ISSN: 1990-9772. Detail
  • MATĚJKA, P.; PLCHOT, O.; NOVOTNÝ, O.; CUMANI, S.; LOZANO DÍEZ, A.; SLAVÍČEK, J.; DIEZ SÁNCHEZ, M.; GRÉZL, F.; GLEMBEK, O.; KAMSALI VEERA, M.; SILNOVA, A.; BURGET, L.; ONDEL YANG, L.; KESIRAJU, S.; ROHDIN, J. BUT- PT System Description for NIST LRE 2017. Proceedings of NIST Language Recognition Workshop 2017. Orlando, Florida: National Institute of Standards and Technology, 2017. p. 1-6. Detail
  • PLCHOT, O.; MATĚJKA, P.; SILNOVA, A.; NOVOTNÝ, O.; DIEZ SÁNCHEZ, M.; ROHDIN, J.; GLEMBEK, O.; BRÜMMER, N.; SWART, A.; PRIETO, J.; GARCIA PERERA, L.; BUERA, L.; KENNY, P.; ALAM, J.; BHATTACHARYA, G. Analysis and Description of ABC Submission to NIST SRE 2016. In Proceedings of Interspeech 2017. Proceedings of Interspeech. Stockholm: International Speech Communication Association, 2017. p. 1348-1352. ISSN: 1990-9772. Detail
  • VESELÝ, K.; BASKAR, M.; DIEZ SÁNCHEZ, M.; BENEŠ, K. MGB-3 BUT System: Low-resource ASR on Egyptian YOUTUBE data. In Proceedings of ASRU 2017. Okinawa: IEEE Signal Processing Society, 2017. p. 368-373. ISBN: 978-1-5090-4788-8. Detail
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