Subband-autocorrelation (SBCOR) analysis is a noise robust acoustic analysis based on filter bank and autocorrelation analysis, and aims to extract the periodicities associated with the inverse of the center frequency in a subband. In this paper, it is derived that SBCOR results in the lateral inhibitive weighting (LIW) processing of the power spectrum, and it is shown that the LIW is significantly effective for noise robust acoustic analysis using a DTW word recognizer. An interpretation of the LIW is also described. A flattening technique of the noise spectral envelope using an LPC inverse filter is applied to speech degraded with noise, and DTW word recognition is performed. The idea of this inverse filtering technique comes from weakening the strong periodic components included in noise. The experimental results using a 32th order LPC inverse filter show that the recognition performance of SBCOR (or LIW) is improved for computer room noise.
['Shoji Kajita', 'Kazuya Takeda', 'Fumitada Itakura']
Spectral weighting of SBCOR for noise robust speech recognition