Robust speaker recognition based on filtering in autocorrelation domain and sub-band feature recombination

This paper presents a new method to improve features derived from filtering in autocorrelation domain, which are called relative autocorrelation sequence mel-frequency cepstral coefficients (RAS-MFCCs), one of the successful features in autocorrelation domain for noise-robust speaker recognition. The RAS-MFCCs are derived by applying temporal filtering to autocorrelation sequences under the assumption that corrupting noise is stationary. However, the use of only the filtered sequences could cause performance degradation due to the use of restricted information, and the assumption that noise is stationary might result in leaving non-stationary noise components in filtered autocorrelation sequences in real environments. To compensate for the restricted information, we propose a multi-streaming feature extraction that uses autocorrelation sequences as well as temporally filtered autocorrelation sequences for feature extraction. Furthermore, a hybrid feature representation, in which the multi-streaming feature extraction and the sub-band feature recombination are combined, is proposed to reduce the noise effects of autocorrelation sequences and the residual-noise effects of temporally filtered autocorrelation sequences. To evaluate the effectiveness of the proposed hybrid speaker recognition system in noisy conditions, we use the TIMIT database and the NTIMIT database. Experiments on the TIMIT database prove the effectiveness of the proposed hybrid method by reducing errors up to 26% and 14% over the conventional RAS-MFCCs in speaker identification and verification, respectively. On the NTIMIT database, the proposed hybrid feature representation provides error reduction of 24% and 18% over the conventional RAS-MFCCs for speaker identification and verification.