关键词: 端点检测, 有限状态矢量量化, 语音识别

Abstract: Speech endpoint detection is very important in speech processing, the traditional algorithm is double threshold comparison based on short-term energy and zero-crossing rate, which is difficult to get accurate test results in lower SNR environments. In addition, the threshold value has a great influence on the test results, but it usually relies on experience, with instability. Because of the shortcoming of traditional method, according to speech inter-
frame correlation, a new algorithm is proposed. In this algorithm, double threshold is used to detect speech signal in condition of lower SNR, which completes primary detection at first. Then, a certain scope of signals vectors gotten in fuzzy frames section of starting and ending point pass finite-state vector quantization after processing, which completes more accurate endpoint detection. The improved endpoint detection algorithm is used for mandarin continuous digit speech recognition, the recognition time is shortened from 0.871 s to 0.719 s and the recognition rate is increased from 81.47% to 89.13%. Experimental results show that new algorithm is more effective than traditional algorithm.

Key words: endpoint detection, finite-state vector quantization, speech recognition