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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 realworld Mandarin-English test corpus in mobile voice search, the proposed schemesdoi:10.1587/transinf.2016sll0004 fatcat:rsoewvqt3jhmbfckaclfsks4pa