Detail publikačního výsledku

Analysis of Multilingual BLSTM Acoustic Model on Low and High Resource Languages

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

Anglický název

Analysis of Multilingual BLSTM Acoustic Model on Low and High Resource Languages

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

The paper provides an analysis of automatic speech recognitionsystems (ASR) based on multilingual BLSTM, where weused multi-task training with separate classification layer foreach language. The focus is on low resource languages, whereonly a limited amount of transcribed speech is available. Insuch scenario, we found it essential to train the ASR systemsin a multilingual fashion and we report superior resultsobtained with pre-trained multilingual BLSTM on this task.The high resource languages are also taken into account andwe show the importance of language richness for multilingualtraining. Next, we present the performance of this techniqueas a function of amount of target language data. The importanceof including context information into BLSTM multilingualsystems is also stressed, and we report increased resilienceof large NNs to overtraining in case of multi-tasktraining.

Anglický abstrakt

The paper provides an analysis of automatic speech recognitionsystems (ASR) based on multilingual BLSTM, where weused multi-task training with separate classification layer foreach language. The focus is on low resource languages, whereonly a limited amount of transcribed speech is available. Insuch scenario, we found it essential to train the ASR systemsin a multilingual fashion and we report superior resultsobtained with pre-trained multilingual BLSTM on this task.The high resource languages are also taken into account andwe show the importance of language richness for multilingualtraining. Next, we present the performance of this techniqueas a function of amount of target language data. The importanceof including context information into BLSTM multilingualsystems is also stressed, and we report increased resilienceof large NNs to overtraining in case of multi-tasktraining.

Klíčová slova

Automatic speech recognition, Multilingualneural networks, Bidirectional Long Short Term Memory

Klíčová slova v angličtině

Automatic speech recognition, Multilingualneural networks, Bidirectional Long Short Term Memory

Autoři

KARAFIÁT, M.; BASKAR, M.; VESELÝ, K.; GRÉZL, F.; BURGET, L.; ČERNOCKÝ, J.

Rok RIV

2019

Vydáno

15.04.2018

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/"
}

Dokumenty

karafiat_icassp2018_0005789