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Proceedings Paper

Research on speech accurate recognition technology based on deep learning DNN-HMM
Author(s): Wanyu Xia; Wu Qiu; Xiancheng Feng
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Paper Abstract

In recent years, with the rapid development of artificial intelligence technology, human auditory intelligence perception has received extensive attention. The human-like auditory intelligent speech separation of robots in complex acoustic environment is studied. Through in-depth learning of key technologies such as DNN-HMM, a new deep network cluster structure, optimization objectives and deep learning algorithm capable of denoising in complex frequency domain are proposed to improve the accuracy of speech recognition, solve the problem of speech separation in human-like hearing in harsh environments, realize high-quality auditory perception in real environments, and enhance intelligence in far-field and complex acoustic environments. Human-computer interaction performance.

Paper Details

Date Published: 14 February 2020
PDF: 4 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114301M (14 February 2020);
Show Author Affiliations
Wanyu Xia, Wuhan Institute of Technology (China)
Hubei Engineering Research Ctr. of Video Image and HD Projection (China)
Wu Qiu, Hubei Yingtong Telecommunication Cable Co., Ltd. (China)
Xiancheng Feng, Wuhan Institute of Technology (China)
Hubei Engineering Research Ctr. of Video Image and HD Projection (China)


Published in SPIE Proceedings Vol. 11430:
MIPPR 2019: Pattern Recognition and Computer Vision
Nong Sang; Jayaram K. Udupa; Yuehuan Wang; Zhenbing Liu, Editor(s)

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