摘要
Emotion mismatch between training and testing will cause system performance decline sharply which is emotional speaker recognition. It is an important idea to solve this problem according to the emotion normalization of test speech. This method proceeds from analysis of the differences between every kind of emotional speech and neutral speech. Besides, it takes the baseband mismatch of emotional changes as the main line. At the same time, it gives the corresponding algorithm according to four technical points which are emotional expansion, emotional shield, emotional normalization and score compensation. Compared with the traditional GMM-UBM method, the recognition rate in MASC corpus and EPST corpus was increased by 3.80% and 8.81% respectively.
Emotion mismatch between training and testing will cause system performance decline sharply which is emotional speaker recognition. It is an important idea to solve this problem according to the emotion normalization of test speech. This method proceeds from analysis of the differences between every kind of emotional speech and neutral speech. Besides, it takes the baseband mismatch of emotional changes as the main line. At the same time, it gives the corresponding algorithm according to four technical points which are emotional expansion, emotional shield, emotional normalization and score compensation. Compared with the traditional GMM-UBM method, the recognition rate in MASC corpus and EPST corpus was increased by 3.80% and 8.81% respectively.
