Spectral estimation using higher-lag autocorrelation coefficients with applications to speech recognition

摘要

In this paper, we introduce a noise robust spectral estimation technique for speech signals that is derived from a windowed one-sided higher-lag autocorrelation sequence. We also introduce a new high dynamic range window design method, and utilise both techniques in a modi ed Mel Frequency Cepstral Coef cient (MFCC) algorithm to produce noise robust speech recognition features. We call the new features Autocorrelation Mel Frequency Cepstral Coef cients (AMFCCs). We compare the recognition performance of AMFCCs to MFCCs for a range of stationary and non-stationary noises on the Aurora II database. We show that the AMFCC features perform as well as MFCCs in clean conditions and have higher noise robustness in noisy conditions.