Multilingual shifting deep bottleneck features for low-resource ASR
Quốc Bảo Nguyễn, Jonas Gehring, Markus Müller, Sebastian Stüker, Alex Waibel · 2014
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.