TY - GEN
T1 - Robust gender classification using neural responses from the model of the auditory system
AU - Mamun, Nursadul
AU - Jassim, Wissam A.
AU - Zilany, Muhammad S.A.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/2/9
Y1 - 2014/2/9
N2 - Human listeners are capable of extracting several information of the speaker such as personality, emotional state, gender, and age using features present in speech signal. The gender classification of a speaker based on his or her speech signal is crucial in telecommunication. This study proposes a gender classification technique using the neural responses of a physiologically-based computational model of the auditory periphery. Neurograms were created from the responses of the model auditory nerve to speech signals. Orthogonal moments were applied on the neurogram to extract features for classification using Gaussian mixture model. The performance of the proposed method was evaluated for eight different types of noise. The result showed a high accuracy for gender classification for both under quiet and noisy conditions. The proposed method could be used as a pre-processor in speaker verification system.
AB - Human listeners are capable of extracting several information of the speaker such as personality, emotional state, gender, and age using features present in speech signal. The gender classification of a speaker based on his or her speech signal is crucial in telecommunication. This study proposes a gender classification technique using the neural responses of a physiologically-based computational model of the auditory periphery. Neurograms were created from the responses of the model auditory nerve to speech signals. Orthogonal moments were applied on the neurogram to extract features for classification using Gaussian mixture model. The performance of the proposed method was evaluated for eight different types of noise. The result showed a high accuracy for gender classification for both under quiet and noisy conditions. The proposed method could be used as a pre-processor in speaker verification system.
KW - Auditory-nerve model
KW - Gaussian Mixture Model
KW - Gender classification
KW - Noisy environment
KW - Orthogonal polynomial
UR - http://www.scopus.com/inward/record.url?scp=84949923421&partnerID=8YFLogxK
U2 - 10.1109/IFESS.2014.7036748
DO - 10.1109/IFESS.2014.7036748
M3 - Conference contribution
AN - SCOPUS:84949923421
T3 - 2014 IEEE 19th International Functional Electrical Stimulation Society Annual Conference, IFESS 2014 - Conference Proceedings
BT - 2014 IEEE 19th International Functional Electrical Stimulation Society Annual Conference, IFESS 2014 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE 19th International Functional Electrical Stimulation Society Annual Conference, IFESS 2014
Y2 - 17 September 2014 through 19 September 2014
ER -