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KARAFIÁT, M. BASKAR, M. VESELÝ, K. GRÉZL, F. BURGET, L. ČERNOCKÝ, J.
Originální název
Analysis of Multilingual BLSTM Acoustic Model on Low and High Resource Languages
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
The paper provides an analysis of automatic speech recognition systems (ASR) based on multilingual BLSTM, where we used multi-task training with separate classification layer for each language. The focus is on low resource languages, where only a limited amount of transcribed speech is available. In such scenario, we found it essential to train the ASR systems in a multilingual fashion and we report superior results obtained with pre-trained multilingual BLSTM on this task. The high resource languages are also taken into account and we show the importance of language richness for multilingual training. Next, we present the performance of this technique as a function of amount of target language data. The importance of including context information into BLSTM multilingual systems is also stressed, and we report increased resilience of large NNs to overtraining in case of multi-task training.
Klíčová slova
Automatic speech recognition, Multilingual neural networks, Bidirectional Long Short Term Memory
Autoři
KARAFIÁT, M.; BASKAR, M.; VESELÝ, K.; GRÉZL, F.; BURGET, L.; ČERNOCKÝ, J.
Vydáno
15. 4. 2018
Nakladatel
IEEE Signal Processing Society
Místo
Calgary
ISBN
978-1-5386-4658-8
Kniha
Proceedings of ICASSP 2018
Strany od
5789
Strany do
5793
Strany počet
5
URL
https://www.fit.vut.cz/research/publication/11720/
BibTex
@inproceedings{BUT155042, author="Martin {Karafiát} and Murali Karthick {Baskar} and Karel {Veselý} and František {Grézl} and Lukáš {Burget} and Jan {Černocký}", title="Analysis of Multilingual BLSTM Acoustic Model on Low and High Resource Languages", booktitle="Proceedings of ICASSP 2018", year="2018", pages="5789--5793", publisher="IEEE Signal Processing Society", address="Calgary", doi="10.1109/ICASSP.2018.8462083", isbn="978-1-5386-4658-8", url="https://www.fit.vut.cz/research/publication/11720/" }