Speaker Recognition in an Emotional Environment
نویسندگان
چکیده
The goal of this paper is to assess the effect of emotional state of a speaker when text-independent speaker identification is performed. Mel-frequency cepstral coefficients are the features of the speech signal used for speaker recognition. For training the speaker models and testing the system, Support Vector Machines are employed. Berlin emotional speech database, which contains 10 different speakers recorded in different emotional situations (happy, angry, fear, bored, sad and neutral) is used. The results show an important influence of the emotional state upon textindependent speaker identification. A possible solution to this issue is finally suggested.
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تاریخ انتشار 2011