AUTHORS: Pallavi Ingale, Sanjay Nalbalwar
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ABSTRACT: Supervised speech segregation for cochannel speech signal can be made easier if we use predetermined speaker’s models instead of taking models for all the population. Here we propose a signal to signal ratio (SSR) independent method to detect speaker identities from a cochannel speech signal with unique speaker specific features for speaker identification. Proposed Kekre’s Transform Cepstral Coefficient (KTCC) features are the robust acoustic features for speaker identification. A text independent speaker identification system is utilized for identifying speakers in short segments of test signal. Gaussian mixture modeling (GMM) classifier is used for the identification task. We compare the proposed method with a system utilizing conventional features called Mel Frequency Cepstral Coefficient (MFCC) features. Spontaneous speech utterances from candidates are taken for experimentation instead of utterances that follow a command like structure with a unique grammatical structure and have a limited word list in speech separation challenge (SSC) corpus. Identification is performed on short segments of the cochannel mixture. Two Speakers who have been identified for most of segments of the cochannel mixture are selected as two speakers detected for the same cochannel mixture. Average speaker detection accuracy of 93.56% is achieved in case of two speaker cochannel mixture for of KTCC features. This method produces best results for cochannel speaker identification even being text independent. Speaker identification performance is also checked for various test segment lengths. KTCC features outperform in speaker identification task even the length of speech segment is very short.
KEYWORDS: Detection of speaker identities, text independent speaker identification, cochannel speech, KTCC
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