Open Access

ARTICLE

Tibetan Multi-Dialect Speech and Dialect Identity Recognition

Yue Zhao¹, Jianjian Yue¹, Wei Song^1,*, Xiaona Xu¹, Xiali Li¹, Licheng Wu¹, Qiang Ji²

1 School of Information and Engineering, Minzu University of China, Beijing, 100081, China.
2 Rensselaer Polytechnic Institute, JEC 7004, Troy NY 12180-3590, USA.

* Corresponding Author: Wei Song. Email: .

Computers, Materials & Continua 2019, 60(3), 1223-1235. https://doi.org/10.32604/cmc.2019.05636

Abstract

Tibetan language has very limited resource for conventional automatic speech recognition so far. It lacks of enough data, sub-word unit, lexicons and word inventories for some dialects. And speech content recognition and dialect classification have been treated as two independent tasks and modeled respectively in most prior works. But the two tasks are highly correlated. In this paper, we present a multi-task WaveNet model to perform simultaneous Tibetan multi-dialect speech recognition and dialect identification. It avoids processing the pronunciation dictionary and word segmentation for new dialects, while, in the meantime, allows training speech recognition and dialect identification in a single model. The experimental results show our method can simultaneously recognize speech content for different Tibetan dialects and identify the dialect with high accuracy using a unified model. The dialect information used in output for training can improve multi-dialect speech recognition accuracy, and the low-resource dialects got higher speech content recognition rate and dialect classification accuracy by multi-dialect and multi-task recognition model than task-specific models.

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Citations