End Point Detection Using Speech-Specific Knowledge for Text-Dependent Speaker Verification

Abstract

This paper proposes a method using speech-specific knowledge to detect the begin and end points of speech under degraded condition. The method is based on vowel-like region (VLR) detection and uses both excitation source and vocal tract system information. Existing method for VLR detection uses excitation source information. Vocal tract system information from dominant resonant frequency is used to eliminate spurious VLRs in background noise. Foreground speech segmentation using excitation and vocal tract system information is carried out to remove spurious VLRs in the background speech region. Better localization of the end points is done using more detailed information about excitation source in terms of glottal activity to detect the sonorant consonants and missed VLRs. To include an unvoiced consonant, obstruent region detection is done at the beginning of the first VLR and at the end of last VLR. Detected begin and end points are evaluated by comparing with manually marked end points as well as by conducting the text-dependent speaker verification experiments. The proposed method performs better than some of the existing techniques.