DNN acoustic modeling with modular multi-lingual feature extraction networks
In this work, we propose several deep neural network architectures that are able to leverage data from multiple languages. Modularity is achieved by training networks for extracting high-level features and for estimating phoneme state posteriors separately, and then combining them for decoding in a hybrid DNN/HMM setup. This approach has been shown to achieve superior performance for single-language systems, and here we demonstrate that feature extractors benefit significantly from being trained as multi-lingual networks with shared hidden representations. We also show that existing mono-lingual networks can be re-used in a modular fashion to achieve a similar level of performance without having to train new networks on multi-lingual data. Furthermore, we investigate in extending these architectures to make use of language-specific acoustic features. Evaluations are performed on a low-resource conversational telephone speech transcription task in Vietnamese, while additional data for acoustic model training is provided in Pashto, Tagalog, Turkish, and Cantonese. Improvements of up to 17.4% and 13.8% over mono-lingual GMMs and DNNs, respectively, are obtained.