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Chinese-English Phone Set Construction for Code-Switching ASR Using Acoustic and DNN-Extracted Articulatory Features by Chung-Hsien Wu; Han-Ping Shen; Yan-Ting Yang is a Computer Science article available to read on EtoBox.

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This study proposes a data-driven approach to phone set construction for code-switching automatic speech recognition (ASR). Acoustic and context-dependent cross-lingual articulatory features (AFs) are incorporated into the estimation of the distance between triphone units for constructing a Chinese-English phone set. The acoustic features of each triphone in the training corpus are extracted for constructing an acoustic triphone HMM. Furthermore, the articulatory features of the "last/first" state of the corresponding preceding/succeeding triphone in the training corpus are used to construct an AF-based GMM. The AFs, extracted using a deep neural network (DNN), are used for code-switching articulation modeling to alleviate the data sparseness problem due to the diverse context-dependent phone combinations in intra-sentential code-switching. The triphones are then clustered to obtain a Chinese-English phone set based on the acoustic HMMs and the AF-based GMMs using a hierarchical triphone clustering algorithm. Experimental results on code-switching ASR show that the proposed method for phone set construction outperformed other traditional methods.

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Author

Chung-Hsien Wu; Han-Ping Shen; Yan-Ting Yang

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2014

Language

EN

Field

Computer Science (Physical Sciences)