Abstract
Several modern speaker recognition systems use a bank of linear filters as the primary step in performing frequency analysis of speech and extracting the acoustics parameters that permit characterizing the speaker identity. In this paper we point up the employ of novel feature set extracted from speech signal. The new skill for extracting these parameters is based on the human auditory system characteristics and relies on the Gammachirp Filterbank to imitate the cochlea frequency resolution with nonlinear resolution according to the equivalent rectangular bandwidth (ERB) scale. For evaluation a comparative study was operated with standard MFCC, and the effect of these differences using an usual HMM/GMM for text independent speaker recognition system, for noisy environments. Performances were test database contaminated with additive noise different real-environment noises were used: car noise provided by Volvo, factory noise and white noise from Noisex92 [1]. Tests were carried out at different SNR levels (-3dB, 0dB, 3dB, 6dB, 12dB).
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
