Distant speaker recognition (DSR) system assumes the microphones are far away from the speaker’s mouth. Also, the position of microphones can vary. Furthermore, various challenges and limitation in terms of coloration, ambient noise and reverberation can bring some difficulties for recognition of the speaker. Although, applying speech enhancement techniques can attenuate speech distortion components, it may remove speaker-specific information and increase the processing time in real-time application. Currently, many efforts have been investigated to develop DSR for commercial viable systems. In this paper, state-of-the-art techniques in DSR such as robust feature extraction, feature normalization, robust speaker modeling, model compensation, dereverberation and score normalization are discussed to overcome the speech degradation components i.e., reverberation and ambient noise. Performance results on DSR show that whenever speaker to microphone distant increases, recognition rates decreases and equal error rate (EER) increases. Finally, the paper concludes that applying robust feature and robust speaker model varying lesser with distant, can improve the DSR performance.
A Generative Model for Score Normalization in Speaker Recognition
