Článek ve sborníku konference

KARAFIÁT Martin, BASKAR Murali K., MATĚJKA Pavel, VESELÝ Karel, GRÉZL František, BURGET Lukáš a 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, s. 719-723. ISSN 1990-9772. Dostupné z: http://www.isca-speech.org/archive/Interspeech_2017/pdfs/1775.PDF
Jazyk publikace:angličtina
Název publikace:2016 BUT Babel system: Multilingual BLSTM acoustic model with i-vector based adaptation
Název (cs):2016 systém VUT pro Babel: Multilingvální BLSTM akustický model s adaptací založenou na i-vektorech
Strany:719-723
Sborník:Proceedings of Interspeech 2017
Konference:Interspeech 2017
Místo vydání:Stockholm, SE
Rok:2017
URL:http://www.isca-speech.org/archive/Interspeech_2017/pdfs/1775.PDF
Časopis:Proceedings of Interspeech, roč. 2017, č. 08, FR
ISSN:1990-9772
DOI:10.21437/Interspeech.2017-1775
Vydavatel:International Speech Communication Association
URL:http://www.fit.vutbr.cz/research/groups/speech/publi/2017/karafiat_interspeech2017_IS171775.pdf [PDF]
Klíčová slova
Automatic speech recognition, Multilingual neural networks, Bidirectional Long Short Term Memory, i-vector,
Anotace
Článek pojednává o 2016 systému VUT pro Babel: Multilingvální BLSTM akustický model s adaptací založenou na i-vektorech.
Abstrakt
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.cs?id=11579}
}

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