Robust speaker identification based on neural response in clean and noisy conditions / Md. Atiqul Islam - Md. Atiqul , Islam (2016)

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Speaker identification (SID) is a biometric technique of determining an unknown speaker's identity using underlying information of his/her speech utterances. It is very essential for security, crime investigation, forensic test, and telephoning. Robust SID under noisy conditions is still a challenging topic in the field of speech processing. Most of the acoustic-feature-based methods fail to achieve robust SID scores under noisy conditions. However, human performance is very robust in noisy environments. The physiologically-based computational model of the auditory nerve (AN) proposed by Zilany and colleagues (2006), which captures almost all of the nonlinearities observed at the level of auditory periphery, was used in this study to obtain a robust SID performance. A neural-response-based novel feature was proposed in this study for both text-dependent and text-independent speaker identification systems. The proposed feature, referred to as neurogram, was computed from the output of the AN model. The training and testing speech signals were taken from three renowned text-independent datasets (YOHO, TIMIT, and TIDIGIT) and a text-dependent audio speech dataset 'UNIVERSITY MALAY A' to evaluate the performance of the proposed system. The speaker modeling was done using speech signals recorded under clean environment whereas testing was done in both clean and noisy conditions. The testing speech signals were contaminated by adding white Gaussian noise, pink noise, and street noise with signal-to-noise ratios (SNRs) ranging from -5 to 15 dB in steps of 5 dB. To develop a speaker model, three standard classifiers were employed in this study such as the Gaussian mixture model (GMM), support vector machine (SVM) and Gaussian mixture model-Universal background model (GMM-UBM). The performance of the proposed neural-feature-based speaker identification was compared to the results from the traditional acoustic-feature-based methods, such a the Mel-frequency cep tral