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Research and Implementation of Speech Recognition Based on HMM/BP

Author: LongBaiLi
Tutor: WuShouEr·SiLaMu
School: Xinjiang University
Course: Applied Computer Technology
Keywords: Speech recognition Hidden Markov models Neural networks Speech feature parameter Hybrid networks
CLC: TN912.3
Type: Master's thesis
Year: 2005
Downloads: 379
Quote: 4
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Abstract


With the development of the modem computer science, the Man Machine Interface has not limited in the keyboard and mouse. More and more new technologies have been applied in the new generation computers, while the progress of the digital speech processing and speech recognition technology made speech a new effective way of input. Speech recognition technology includes many scientific fields such as the acoustics, the linguistics, digital signal processing, computer science, artificial neural network and so on.。The characteristics of speech recognition brings many difficulties to this technology. The process of computer speech recognition is almost an imitation of the speech recognition of human. Current main technology of speech recognition is based on the theory of statistic pattern recognition. This article introduce the basic conception, the common method and characteristics of isolated word recognition system. And analyse the extraction of LPCC and MFCC from the speech signal at time-domain and frequency-domain. Through analyzing the influence of endpoint detection, and combine the method of improving robustness ,introduce dynamic window size .Meanwhile, analyzing the basic theory of from three questions (evaluation question , decode question , training question) and the application for speech recognition. This article finally realize a small, isolated word speech recognition system. This system realize the extraction of feature parameter, the training of speech model parameter and recognition of the recorded speech. This article use MFCC as feature parameter, the HMM model used for speech model. And introduce BP neural network to system for the second recognition, the HMM is applied as the front-end to process the time sequence of speech and the primary recognition information is provided in this step .In the next step,BPNN is applied as the back end and because of its superior functions of pattern classification and generalization,the primary recognition information is non-linearly mapped into the secondary recognition information.The final recognition procedure is accomplished with the two kinds of recognition information. Experiments prove that using this robust model,recognition rate can be noticeably improved in noisy environment.Finally, this article study the Uighur speech recognition and realize.

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CLC: > Industrial Technology > Radio electronics, telecommunications technology > Communicate > Electro-acoustic technology and speech signal processing > Speech Signal Processing
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