Significance of Complementary Spectral Features for Speaker Recognition
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2013, Vol 2, Issue 8
Abstract
Speaker recognition is the process of automatically recognizing who is speaking on the basis of individual information included in speech waves.. The most popular features for speaker recognition are Mel frequency cepstral coefficients (MFCCs) and linear prediction cepstral Coefficients (LPCCs). These features are used extensively because they characterize the vocal tract configuration which is known. Motivated by the physiological significance of the vocal source and vocal tract system in speech production this paper present application of these feature as complementary information sources for a speaker recognition for improved performance and robustness. Speaker recognition makes it possible to user's voice to verify their identity and control access to services such as voice dialing, banking by telephone, telephone shopping, database access services, information services, voice mail, security control for confidential information areas, and remote access to computersto be highly speakerdependent.
Authors and Affiliations
Piyush Lotia, Dr M R Khan
Keywords
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