Robust speech feature extraction using SBCOR analysis

The paper describes to what extent subband-autocorrelation (SBCOR) analysis is robust against waveform distortion and noises. The SBCOR analysis, which has been already proposed, is a signal processing technique based on subband processing and autocorrelation analysis so as to extract periodicities present in speech signals. First, it is shown that SBCOR is robust against severe waveform distortions such as zero-crossing. Although the zero-crossing distortion deteriorates the performance of conventional recognition systems, such distorted signals are still intelligible for humans. The experimental results using a DTW word recognition show that the SBCOR (Q=1.0) performs about 19% higher than smoothed group delay spectrum (SGDS), when the test signals are distorted by zero-crossing. Second, it is shown that SBCOR is more robust against multiplicative signal-dependent white noise, Gaussian white noise, and a human speech noise than SGDS. The validity of the SBCOR is larger when the noise is white than when the noise is the human speech noise