Title :
Multilingual shifting deep bottleneck features for low-resource ASR
Author :
Quoc Bao Nguyen ; Gehring, Jonas ; Muller, Mathias ; Stuker, Sebastian ; Waibel, Alex
Author_Institution :
Karlsruhe Inst. of Technol., Karlsruhe, Germany
Abstract :
In this work, we propose a deep bottleneck feature architecture that is able to leverage data from multiple languages. We also show that tonal features are helpful for non-tonal languages. Evaluations are performed on a low-resource conversational telephone speech transcription task in Bengali, while additional data for DBNF training is provided in Assamese, Pashto, Tagalog, Turkish, and Vietnamese. We obtain relative reductions of up to 17.3% and 9.4% WER over mono-lingual GMMs and DBNFs, respectively.
Keywords :
Gaussian processes; feature extraction; mixture models; natural language processing; speech recognition; Assamese; Bengali; DBNF training; Pashto; Tagalog; Turkish; Vietnamese; WER; low-resource ASR; low-resource conversational telephone speech transcription task; monolingual GMM; multilingual shifting deep bottleneck features; nontonal languages; tonal features; Feature extraction; Mel frequency cepstral coefficient; Speech; Speech recognition; Standards; Training; Deep Neural Networks; Low-Resource ASR; Multilingual Deep bottleneck features;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on
Conference_Location :
Florence
DOI :
10.1109/ICASSP.2014.6854676
