Conference paper

KARAFIÁT Martin, BASKAR Murali K., MATĚJKA Pavel, VESELÝ Karel, GRÉZL František, BURGET Lukáš and ČERNOCKÝ Jan. 2016 BUT Babel system: Multilingual BLSTM acoustic model with i-vector based adaptation. In: Proceedings of Interspeech 2017. Stockholm: International Speech Communication Association, 2017, pp. 719-723. ISSN 1990-9772. Available from: http://www.isca-speech.org/archive/Interspeech_2017/pdfs/1775.PDF
Publication language:english
Original title:2016 BUT Babel system: Multilingual BLSTM acoustic model with i-vector based adaptation
Title (cs):2016 systém VUT pro Babel: Multilingvální BLSTM akustický model s adaptací založenou na i-vektorech
Pages:719-723
Proceedings:Proceedings of Interspeech 2017
Conference:Interspeech 2017
Place:Stockholm, SE
Year:2017
URL:http://www.isca-speech.org/archive/Interspeech_2017/pdfs/1775.PDF
Journal:Proceedings of Interspeech, Vol. 2017, No. 08, FR
ISSN:1990-9772
DOI:10.21437/Interspeech.2017-1775
Publisher:International Speech Communication Association
URL:http://www.fit.vutbr.cz/research/groups/speech/publi/2017/karafiat_interspeech2017_IS171775.pdf [PDF]
Keywords
Automatic speech recognition, Multilingual neural networks, Bidirectional Long Short Term Memory, i-vector,
Annotation
This article is about the 2016 BUT Babel system: Multilingual BLSTM acoustic model with i-vector based adaptation.
Abstract
The paper provides an analysis of BUT automatic speech recognition systems (ASR) built for the 2016 IARPA Babel evaluation. The IARPA Babel program concentrates on building ASR system for many low resource languages, where only a limited amount of transcribed speech is available for each language. In such scenario, we found essential to train the ASR systems in a multilingual fashion. In this work, we report superior results obtained with pre-trained multilingual BLSTM acoustic models, where we used multi-task training with separate classification layer for each language. The results reported on three Babel Year 4 languages show over 3% absolute WER reductions obtained from such multilingual pre-training. Experiments with different input features show that the multilingual BLSTM performs the best with simple log-Mel-filter-bank outputs, which makes our previously successful multilingual stack bottleneck features with CMLLR adaptation obsolete. Finally, we experiment with different configurations of i-vector based speaker adaptation in the mono- and multi-lingual BLSTM architectures. This results in additional WER reductions over 1% absolute.
BibTeX:
@INPROCEEDINGS{
   author = {Martin Karafi{\'{a}}t and K. Murali Baskar and
	Pavel Mat{\v{e}}jka and Karel Vesel{\'{y}} and
	Franti{\v{s}}ek Gr{\'{e}}zl and Luk{\'{a}}{\v{s}}
	Burget and Jan {\v{C}}ernock{\'{y}}},
   title = {2016 BUT Babel system: Multilingual BLSTM acoustic
	model with i-vector based adaptation},
   pages = {719--723},
   booktitle = {Proceedings of Interspeech 2017},
   journal = {Proceedings of Interspeech},
   volume = {2017},
   number = {08},
   year = {2017},
   location = {Stockholm, SE},
   publisher = {International Speech Communication Association},
   ISSN = {1990-9772},
   doi = {10.21437/Interspeech.2017-1775},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php.en?id=11579}
}

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