15th Annual Conference of the International Speech Communication Association

September 14-18, 2014

Development of Bilingual ASR System for MediaParl Corpus

Petr Motlicek (1), David Imseng (1), Milos Cernak (1), Namhoon Kim (2)

(1) Idiap Research Institute, Switzerland
(2) Samsung Electronics, Korea

The development of an Automatic Speech Recognition (ASR) system for the bilingual MediaParl corpus is challenging for several reasons: (1) reverberant recordings, (2) accented speech, and (3) no prior information about the language. In that context, we employ frequency domain linear prediction-based (FDLP) features to reduce the effect of reverberation, exploit bilingual deep neural networks applied in Tandem and hybrid acoustic modeling approaches to significantly improve ASR for accented speech and develop a fully bilingual ASR system using entropy-based decoding-graph selection. Our experiments indicate that the proposed bilingual ASR system performs similar to a language-specific ASR system if approximately five seconds of speech are available.

Full Paper

Bibliographic reference.  Motlicek, Petr / Imseng, David / Cernak, Milos / Kim, Namhoon (2014): "Development of bilingual ASR system for MediaParl corpus", In INTERSPEECH-2014, 1391-1394.