Influence of Autocorrelation Lag Ranges on Robust Speech Recognition

It is generally believed that the lower-lag autocorrelation coefficients carry information about the spectral envelope and the higher-lag autocorrelation coefficients are more related to pitch information. In this paper, we use lower-lag and higher-lag ranges of the autocorrelation function separately for deriving speech recognition features, and investigate their role in terms of speech recognition performance. The state-of-the-art MFCC (mel frequency cepstral coefficient) features use the whole autocorrelation function in their computation and are used here as a benchmark in our experiments. Our recognition results from the Aurora II corpus show that the higher-lag autocorrelation coefficients perform as well as the whole autocorrelation function for clean speech, and provide better performance for noisy speech, while lower-lag autocorrelation coefficients are not as effective in this aspect.