期刊
IEEE SIGNAL PROCESSING LETTERS
卷 13, 期 1, 页码 52-55出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2005.860538
关键词
autoassociative neural network; glottal closure instant; linear prediction (LP) residual; residual phase; speaker verification
The objective of this letter is to demonstrate the complementary nature of speaker-specific information present in the residual,phase in comparison with the information present in the conventional mel-frequency cepstral coefficients (MFCCs). The residual phase is derived from speech signal by linear prediction analysis. Speaker recognition studies are conducted on the NIST-2003 database using the proposed residual phase and the existing MFCC features. The speaker recognition system based on the residual phase gives an equal error rate (EER) of 22%, and the system using the MFCC features gives an EER of 14%. By combining the evidence from both the residual phase and the MFCC features, an EER of 10.5% is obtained, indicating that speaker-specific excitation information is present in the residual phase. This information is useful since it is complementary to that of MFCCs.
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