Perception auditory scene analysis for speaker recognition

For the decreasing robustness of missing data features method of speaker recognition in low-SNRs environ- ment, a missing data features extraction method based on Perception Auditory Scene Analysis is proposed. Missing data features spectrum is calculated firstly. And perception speech content is solved by speech perception characteristic. After speech enhancement based on auditory perceptual characteristic and a 2 dimension enhancement for spectrogram, speech distribution is obtained from noisy speech, which is combined with perception speech content and missing inten- sity parameter to extract Perception Auditory Factor. Perception Auditory Factor and missing data features spectrum resolve the features extraction process into different auditory scenes, which are treated respectively in order to improve robustness of speaker recognition system. Experimental results show that, the proposed method improves the robustness to other five methods in four different noisy low-SNRs environments from -10 dB to 10 dB. The average recognition rates of the proposed method increase 26.0%, 19.6%, 12.7%, 4.6% and 6.5% respectively. The proposed method is to find out the robust features in time- frequency domain, and more suitable for speaker recognition in low-SNRs environment.