A Vector piecewise polynomial (VPP) approximation algorithm is proposed for environ-ment compensation of speech signals degraded by both additive and convolutive noises. By investi-gating the model of the telephone en...A Vector piecewise polynomial (VPP) approximation algorithm is proposed for environ-ment compensation of speech signals degraded by both additive and convolutive noises. By investi-gating the model of the telephone environment, we propose a piecewise polynomial, namely twolinear polynomials and a quadratic polynomial, to approximate the environment function precisely.The VPP is applied either to the stationary noise, or to the non stationary noise. In the first case,the batch EM is used in log-spectral domain; in the second case the recursive EM with iterativestochastic approximation is developed in cepstral domain. Both approaches are based on the mini-mum mean squared error (MMSE) sense. Experimental results are presented on the application ofthis approach in improving the performance of Mandarin large vocabulary continuous speech recog-nition (LVCSR) due to the background noises and different transmission channels (such as fixedtelephone line and GSM). The method can reduce the average character error rate (CER) by a-bout 18%.展开更多
文摘A Vector piecewise polynomial (VPP) approximation algorithm is proposed for environ-ment compensation of speech signals degraded by both additive and convolutive noises. By investi-gating the model of the telephone environment, we propose a piecewise polynomial, namely twolinear polynomials and a quadratic polynomial, to approximate the environment function precisely.The VPP is applied either to the stationary noise, or to the non stationary noise. In the first case,the batch EM is used in log-spectral domain; in the second case the recursive EM with iterativestochastic approximation is developed in cepstral domain. Both approaches are based on the mini-mum mean squared error (MMSE) sense. Experimental results are presented on the application ofthis approach in improving the performance of Mandarin large vocabulary continuous speech recog-nition (LVCSR) due to the background noises and different transmission channels (such as fixedtelephone line and GSM). The method can reduce the average character error rate (CER) by a-bout 18%.
