Publication Details
BUT/Phonexia Bottleneck Feature Extractor
Matějka Pavel, Ing., Ph.D. (DCGM)
Glembek Ondřej, Ing., Ph.D.
Plchot Oldřich, Ing., Ph.D. (DCGM)
Novotný Ondřej, Ing., Ph.D.
Grézl František, Ing., Ph.D. (DCGM)
Schwarz Petr, Ing., Ph.D. (DCGM)
Černocký Jan, prof. Dr. Ing. (DCGM)
bottlneck feature extractor, speech recognition, language recognition
This paper complements the public release of the BUT/Phonexia bottleneck (BN) feature extractor. Starting with a brief history of Neural Network (NN)-based and BN-based approaches to speech feature extraction, it describes the structure of the released software. It follows by describing the three provided NNs: the first two trained on the US English Fisher corpus with monophone-state and tied-state targets, and the third network trained in a multi-lingual fashion on 17 Babel languages. The NNs were technically trained to classify acoustic units, however the networks were optimized with respect to the language recognition task, which is the main focus of this paper. Nevertheless, it is worth noting that apart from language recognition, the provided software can be used with any speech-related task. The paper concludes with a comprehensive summary of the results obtained on the NIST 2015 and 2017 Language Recognition Evaluations tasks.
@inproceedings{BUT155076,
author="Anna {Silnova} and Pavel {Matějka} and Ondřej {Glembek} and Oldřich {Plchot} and Ondřej {Novotný} and František {Grézl} and Petr {Schwarz} and Jan {Černocký}",
title="BUT/Phonexia Bottleneck Feature Extractor",
booktitle="Proceedings of Odyssey 2018",
year="2018",
journal="Proceedings of Odyssey: The Speaker and Language Recognition Workshop Odyssey 2014, Joensuu, Finland",
volume="2018",
number="6",
pages="283--287",
publisher="International Speech Communication Association",
address="Les Sables d´Olonne",
doi="10.21437/Odyssey.2018-40",
issn="2312-2846",
url="https://www.fit.vut.cz/research/publication/11789/"
}