A recognition algorithm without the ending-point detection of Chinese based on the DTW and HMM unified model

Describes a characteristic of Chinese speech, according to the duration time of Chinese consonant, and a recognition algorithm without end-point detection is proposed. Three situations in this algorithm have been pointed out, but the same result has been obtained from them. Compared with the traditional method, in this algorithm, it is not necessary to decide the end-point of speech signals. From the beginning, feature vectors, which consist of 15-order cepstrum coefficients and the average energy of each frame, are extracted in frames (length of each frame is 20 millisecond, the overlapping between two frames is 50%). By introducing the self-loop of the silent segment of the discrete time warping (DTW) and HMM unified model (DHUM), this algorithm is successfully implemented. In recognition of 99 similar words of Chinese, a first candidate recognition rate of 94.95% is obtained. To study the robustness of the algorithm, auditory representation of speech signals is also employed to obtained feature vectors. From the comparison of different feature-extraction methods, the conclusion is obtained that if an auditory feature is accepted for feature vectors, the robustness of the algorithm will be better; and because of the superiority of auditory representation to describe the characteristics of the silent segment of speech, the auditory feature-vector is more suitable for this algorithm.