Title :
Recognition of noisy speech using cumulant-based linear prediction analysis
Author :
Paliwal, K.K. ; Sondhi, M.M.
Author_Institution :
AT&T Bell Labs., Murray Hill, NJ, USA
Abstract :
The use of cumulant-based LP (linear prediction) analysis for speech recognition in the presence of noise is proposed. This method assumes the speech signal to be non-Gaussian. It is shown that cepstral coefficients derived by this method are quite insensitive to additive Gaussian noise which can be white or colored. The performance of a recognizer based on these estimates is compared to the performance of one that uses LP estimates derived from the autocorrelation function. It is found that at low SNR (below about 20 dB) the cumulant-based estimates outperform the autocorrelation-based estimates. At higher SNRs the reverse is true. The reasons for this behavior are not yet understood. However, it is shown that, by combining the two estimates, one can achieve recognition accuracy that is better than that of the conventional recognizer at all SNRs
Keywords :
filtering and prediction theory; random noise; speech analysis and processing; speech recognition; additive Gaussian noise; autocorrelation function; cepstral coefficients; cumulant-based linear prediction analysis; recognition accuracy; speech recognition; Acoustics; Additive noise; Autocorrelation; Degradation; Gaussian noise; Signal analysis; Speech analysis; Speech enhancement; Speech recognition; Testing;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference on
Conference_Location :
Toronto, Ont.
Print_ISBN :
0-7803-0003-3
DOI :
10.1109/ICASSP.1991.150368