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Speech with various emotions aggravates the performance of speaker recognition system. The existing speaker modeling disregards the match of the emotional state between training and testing speech, and the systems suffer the lapsus of the emotion recognition as to practical application. We propose an alternative approach that exploits the prosodic difference to cluster affective speech, and then builds...
Speech with various emotions aggravates the performance of speaker recognition systems. In this paper, a novel score normalization approach called pitch envelope based frame level score reweighted (PFLSR) algorithm is introduced to compensate the influence of the affective speech on speaker recognition. The approach assumes that the maximum likelihood model is not easily changed with the expressive...
In this paper, a large emotional speech database MASC (Mandarin affective speech corpus) is introduced. The database contains recordings of 68 native speakers (23 female and 45 male) and five kinds of emotional states: neutral, anger, elation, panic and sadness. Each speaker pronounces 5 phrases, 10 sentences for three times for each emotional states and 2 paragraphs only for neutral. These materials...
One of the largest challenges in speaker recognition applications is dealing with speaker-emotion variability. In this paper, we further investigate the rules based feature modification for robust speaker recognition with emotional speech. Specifically, we learn the rules of prosodic features modification from a small amount of the content matched source-target pairs. Features with emotion information...
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