关键词: 异常语音, 说话人识别, 变异特征加权, K-L 距离, 加权因子

Abstract:

As the commonly-used weighting algorithm is inefficient in tracking the abnormal feature of abnormal speech,a speaker recognition algorithm for abnormal speech is proposed based on the abnormal feature weighting. In this algorithm,first,a feature template of normal speech is established by computing the probability distribution of MFCC features of each order in a large number of normal speech samples. Then,the K-L distance and the Euclidean distance are used to measure the differences between a given test speech and the normal speech templates and to further determine the K-L and the Euclidean weighting factors. Finally,the two weighting factors are used to weight the MFCC features of the test speech,and the weighted MFCC features are input in the Gaussian mixture model for the speaker recognition with abnormal speech. Experimental results show that the global recognition rates of the speaker recognition algorithms based on the K-L weighting and the Euclidean weighting are respectively 46.61% and 42.25%,while those of the algorithms with and without the weighting of speaker recognition contribution of each order feature are respectively only 39.68% and 36.36%.

Key words: abnormal speech, speaker recognition, abnormal feature weighting, K-L distance, weighting factor