Research On Auditory Characteristics And Robust Speech Recognition Algorithms

The technology of speech recognition inaugurates a new era of the communication between human and machine. The speech recognition systems can be applied in a widely field, such as industry, military, business, finance, service, medical treatment, daily life, etc. For the environmental mismatch, the performance of the recognition systems is dramatically deteriorated. So the robustness becomes the focus of the research of speech recognition. Here, the current technology of speech recognition and robust speech recognition, the auditory characteristic of human, the estimation principles applied in speech recognition, the approaches by which noise affects the recognition performance are researched in detail. According to the auditory characteristics, the spectral difference between speech signals and noise, the different effects on speech recognition models caused by noise in different bands, several kinds of the robust speech recognition algorithms have been presented to improve the performance of the speech recognition systems in noisy environment with the different model schemes, the different principles of estimation, the different match methods, the different analysis of the reliability of the information.The technology of model analysis and compensation is an important way of robust speech recognition. According to amount of the theoretical analyses and researches, based on Fletcher-Allen principle, the nonlinear class estimation algorithm using parallel sub-band HMM maximum a posteriori probability adaptation and the nonlinear sub-band maximum a posteriori statistical matching algorithm have been proposed. The nonlinear class estimation algorithm adopts MAP principle, linear mapping, BP network to recognize the speech signal. The reliability analysis of the information utilizes the signal to noise rate in the algorithm. And a new prior information estimation method is presented, which efficiently reflects the noisy environment. The nonlinear statistical match algorithm is a new algorithm which combines MAP statistical match with nonlinear mapping. The experiments show these researches evidently improve the performance of speech recognition in noisy environment.Based on the phenomena of the auditory stream grouping, the multi-band robust speech recognition algorithms according to noise corruption assumption have been researched here. The multi-band asynchronous mode is researched firstly. The multi-band maximum likelihood robust speech recognition algorithm is based on the multi-band asynchronous mode, which utilizes the maximum likelihood linear mapping, linear analysis or discriminative analysis. According to the specialty of the multi-band analysis, the discriminative multi-band maximum a posteriori...