Research For Algorithm Of Speech Recognition Based On WD/HMM

Speech recognition is one of main branches in the information and technology field. How to improve the robustness of a recognizer in presence of background noise has been a vital difficulty. In speech recognition, the first step is the extraction of speech features, whose capability are crucial to the performance of the whole speech recognition system. Therefore, this paper aims at improving the performance of speech recognition system in noisy environment, focuses on robust speech feature coefficients extraction, studies robust speech recognition system in noise.Based on deeply comprehension in the fundamentals of speech recognition, some kinds of widely-used speech feature coefficients are introduced firstly. Secondly, the time-frequency analysis method of nonstationary random signal is discussed, which is named Wigner Distribution (WD). Based on the time-varying character of speech, this paper makes full use of the excellent characters in WD and applies it to speech processing. Then WD is combined with homomorphic processing technique to compute two kinds of feature coefficients, which are cepstral coefficients based on WD, named WD-MFCC, and cepstral coefficients based on symmetrical correlation function, named WV-MFCC. At the same time, a spectrogram is derived from WD of speech. Lastly, the application of Hidden Markov Model (HMM) to speech recognition is deeply studied. The two kinds of coefficients are applied to a speech recognition system which employs HMM as the recognizer, as well as the previously introduced feature coefficients. What's more, the last part of this paper has simulated and analyzed the robustness of the speech recognition system in noise when applying different speech features. The simulation results shows that the new feature coefficients proposed in this paper can significantly improve the robustness of speech recognition system.