Multi-task Recurrent Model for Speech and Speaker Recognition

Zhiyuan Tang; Lantian Li; Dong Wang

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arXiv · 1603.09643

Multi-task Recurrent Model for Speech and Speaker Recognition

Zhiyuan Tang·Lantian Li·Dong Wang

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Abstract

Although highly correlated, speech and speaker recognition have been regarded as two independent tasks and studied by two communities. This is certainly not the way that people behave: we decipher both speech content and speaker traits at the same time. This paper presents a unified model to perform speech and speaker recognition simultaneously and altogether. The model is based on a unified neural network where the output of one task is fed to the input of the other, leading to a multi-task recurrent network. Experiments show that the joint model outperforms the task-specific models on both the two tasks.

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BibTeX RIS

Zhiyuan Tang, Lantian Li, Dong Wang. 2016-03-31. Multi-task Recurrent Model for Speech and Speaker Recognition. https://arxiv.org/abs/1603.09643

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