TY - GEN
T1 - Speaker recognition using neural responses from the model of the auditory system
AU - Razali, Noor Fadzilah
AU - Jassim, Wissam A.
AU - Roohisefat, Leyla
AU - Zilany, M. S.A.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/1/27
Y1 - 2014/1/27
N2 - Speaker recognition is a process of determining a person's identity using features in speech signals. In this study, a new speaker recognition (identification and verifica-tion) system is proposed using the responses from a computational model of the auditory system. A neurogram (2D) was constructed from the responses of the model of auditory nerve fibers for a range of characteristic frequencies. The proposed neurogram based speaker recognition system was trained and tested using a Gaussian mixture model classification technique. The performance of the proposed method was evaluated for both clean speech and speech under noisy environment. The result of the proposed method was compared to a traditional speaker recognition technique, referred to as the mel-frequency cepstral coefficient method. The proposed method showed better performance than the traditional approach, especially under noisy conditions. The proposed method could be applied in security and voice recognition systems.
AB - Speaker recognition is a process of determining a person's identity using features in speech signals. In this study, a new speaker recognition (identification and verifica-tion) system is proposed using the responses from a computational model of the auditory system. A neurogram (2D) was constructed from the responses of the model of auditory nerve fibers for a range of characteristic frequencies. The proposed neurogram based speaker recognition system was trained and tested using a Gaussian mixture model classification technique. The performance of the proposed method was evaluated for both clean speech and speech under noisy environment. The result of the proposed method was compared to a traditional speaker recognition technique, referred to as the mel-frequency cepstral coefficient method. The proposed method showed better performance than the traditional approach, especially under noisy conditions. The proposed method could be applied in security and voice recognition systems.
KW - auditory nerve model
KW - Gaussian Mixture Model
KW - mel-frequency cepstral coefficient
KW - neurogram
KW - speaker identification
KW - speaker verification
UR - http://www.scopus.com/inward/record.url?scp=84946687232&partnerID=8YFLogxK
U2 - 10.1109/ISPACS.2014.7024428
DO - 10.1109/ISPACS.2014.7024428
M3 - Conference contribution
AN - SCOPUS:84946687232
T3 - 2014 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2014
SP - 76
EP - 79
BT - 2014 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2014
Y2 - 1 December 2014 through 4 December 2014
ER -