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

Yue Zhao1, Jianjian Yue1, Wei Song1,*, Xiaona Xu1, Xiali Li1, Licheng Wu1, Qiang Ji2

School of Information and Engineering, Minzu University of China , Beijing, 100081, China.
Rensselaer Polytechnic Institute, 110 Eighth Street, Troy NY 12180-3590, USA.

*Corresponding Author: Wei Song. Email: 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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Cite This Article

APA Style
Zhao, Y., Yue, J., Song, W., Xu, X., Li, X. et al. (2019). Tibetan multi-dialect speech recognition using latent regression bayesian network and end-to-end mode. Journal on Internet of Things, 1(1), 17-23. https://doi.org/10.32604/jiot.2019.05866
Vancouver Style
Zhao Y, Yue J, Song W, Xu X, Li X, Wu L, et al. Tibetan multi-dialect speech recognition using latent regression bayesian network and end-to-end mode. J Internet Things . 2019;1(1):17-23 https://doi.org/10.32604/jiot.2019.05866
IEEE Style
Y. Zhao et al., “Tibetan Multi-Dialect Speech Recognition Using Latent Regression Bayesian Network and End-To-End Mode,” J. Internet Things , vol. 1, no. 1, pp. 17-23, 2019. https://doi.org/10.32604/jiot.2019.05866



cc Copyright © 2019 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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