Abstract:

Based on analysis of acoustic and perception characteristics of whispered mandarin speech, a tone detection method using ant colony clustering is proposed. A multi-dimension feature vector consisted of amplitude envelope, formant, vocal tract length, average firing rate of auditory nerves is chosen as the mainly cue for whispered tone. The feature vectors are clustered by ant colony algorithm and then input to regional supervised feature mapping neural network for training and recognizing. The experiment results show that 87.5% average recognition accuracy could be reached and the performance of proposed method is improved significantly compared with classical models.

Key words: whispered speech, tone detection, ant colony clustering, neural network