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Multi-Task Learning in Deep Neural Networks for Mandarin-English Code-Mixing Speech Recognition
Mengzhe CHEN Jielin PAN Qingwei ZHAO Yonghong YAN
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2016/10/01
Online ISSN: 1745-1361
Type of Manuscript: Special Section LETTER (Special Section on Recent Advances in Machine Learning for Spoken Language Processing)
Category: Acoustic modeling
multi-task learning, deep neural network, Mandarin-English code mixing, speech recognition,
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Multi-task learning in deep neural networks has been proven to be effective for acoustic modeling in speech recognition. In the paper, this technique is applied to Mandarin-English code-mixing recognition. For the primary task of the senone classification, three schemes of the auxiliary tasks are proposed to introduce the language information to networks and improve the prediction of language switching. On the real-world Mandarin-English test corpus in mobile voice search, the proposed schemes enhanced the recognition on both languages and reduced the relative overall error rates by 3.5%, 3.8% and 5.8% respectively.