Open Access

ARTICLE

Tibetan Multi-Dialect Speech Recognition Using Latent Regression Bayesian Network and End-To-End Mode

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, 110 Eighth Street, Troy NY 12180-3590, USA.

*Corresponding Author: Wei Song. Email: .

Journal on Internet of Things 2019, 1(1), 17-23. https://doi.org/10.32604/jiot.2019.05866

Abstract

We proposed a method using latent regression Bayesian network (LRBN) to extract the shared speech feature for the input of end-to-end speech recognition model. The structure of LRBN is compact and its parameter learning is fast. Compared with Convolutional Neural Network, it has a simpler and understood structure and less parameters to learn. Experimental results show that the advantage of hybrid LRBN/Bidirectional Long Short-Term Memory-Connectionist Temporal Classification architecture for Tibetan multi-dialect speech recognition, and demonstrate the LRBN is helpful to differentiate among multiple language speech sets.

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Keywords

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