Research on speech accurate recognition technology based on deep learning DNN-HMM

Wanyu Xia; Wu Qiu; Xiancheng Feng

doi:10.1117/12.2539467

14 February 2020 Research on speech accurate recognition technology based on deep learning DNN-HMM

Wanyu Xia, Wu Qiu, Xiancheng Feng

Proceedings Volume 11430, MIPPR 2019: Pattern Recognition and Computer Vision; 114301M (2020) https://doi.org/10.1117/12.2539467
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

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.

Citation Download Citation

Wanyu Xia, Wu Qiu, and Xiancheng Feng "Research on speech accurate recognition technology based on deep learning DNN-HMM", Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114301M (14 February 2020); https://doi.org/10.1117/12.2539467

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