TY - JOUR
T1 - Frame Selection for Robust Speaker Identification
T2 - A Hybrid Approach
AU - Prasad, Swati
AU - Tan, Zheng Hua
AU - Prasad, Ramjee
PY - 2017/11
Y1 - 2017/11
N2 - Identification of a person using voice is a challenging task under environmental noises. Important and reliable frame selection for feature extraction from the time-domain speech signal under noise can play a significant role in improving speaker identification accuracy. Therefore, this paper presents a frame selection method using hybrid technique, which combines two techniques, namely, voice activity detection (VAD) and variable frame rate (VFR) analysis. It efficiently captures the active speech part, the changes in the temporal characteristics of the speech signal, taking into account the signal-to-noise ratio, and thereby speaker-specific information. Experimental results on noisy speech, generated by artificially adding various noise signals to the clean YOHO speech at different SNRs have shown improved results for the frame selection by the hybrid technique in comparison with any one of the techniques used for the hybrid. The proposed hybrid technique outperformed both the VFR and the widely used Gaussian statistical model based VAD method for all noise scenarios at different SNRs, except for the Babble noise corrupted speech at 5 dB SNR, for which, VFR performed better. Considering the average identification accuracies of different noise scenarios, a relative improvement of 9.79% over the VFR, and 18.05% over the Gaussian statistical model based VAD method has been achieved.
AB - Identification of a person using voice is a challenging task under environmental noises. Important and reliable frame selection for feature extraction from the time-domain speech signal under noise can play a significant role in improving speaker identification accuracy. Therefore, this paper presents a frame selection method using hybrid technique, which combines two techniques, namely, voice activity detection (VAD) and variable frame rate (VFR) analysis. It efficiently captures the active speech part, the changes in the temporal characteristics of the speech signal, taking into account the signal-to-noise ratio, and thereby speaker-specific information. Experimental results on noisy speech, generated by artificially adding various noise signals to the clean YOHO speech at different SNRs have shown improved results for the frame selection by the hybrid technique in comparison with any one of the techniques used for the hybrid. The proposed hybrid technique outperformed both the VFR and the widely used Gaussian statistical model based VAD method for all noise scenarios at different SNRs, except for the Babble noise corrupted speech at 5 dB SNR, for which, VFR performed better. Considering the average identification accuracies of different noise scenarios, a relative improvement of 9.79% over the VFR, and 18.05% over the Gaussian statistical model based VAD method has been achieved.
KW - Biometric
KW - Frame selection
KW - Robust speaker identification
KW - Variable frame rate (VFR)
UR - http://www.scopus.com/inward/record.url?scp=85019683611&partnerID=8YFLogxK
U2 - 10.1007/s11277-017-4544-1
DO - 10.1007/s11277-017-4544-1
M3 - Journal article
AN - SCOPUS:85019683611
SN - 0929-6212
VL - 97
SP - 933
EP - 950
JO - Wireless Personal Communications
JF - Wireless Personal Communications
IS - 1
ER -