Title :
SURE-MSE speech enhancement for robust speech recognition
Author :
Zheng, Nengheng ; Li, Xia ; Blu, Thierry ; Lee, Tan
Author_Institution :
Coll. of Inf. Eng., Shenzhen Univ., Shenzhen, China
fDate :
Nov. 29 2010-Dec. 3 2010
Abstract :
This paper presents a new approach to enhancing noisy (white Gaussian noise) speech signals for robust speech recognition. It is based on the minimization of an estimate of denoising MSE (known as SURE) and does not require any hypotheses on the original signal. The enhanced signal is obtained by thresholding coefficients in the DCT domain, with the parameters in the thresholding functions being specified through the minimization of the SURE. Thanks to a linear parametrization, this optimization is very cost-effective. This method also works well for non-white noise with a noise whitening processing before the optimization. We have performed automatic speech recognition tests on a subset of the AURORA 2 database, to compare our method with different denoising strategies. The results show that our method brings a substantial increase in recognition accuracy.
Keywords :
AWGN; discrete cosine transforms; mean square error methods; minimisation; signal denoising; speech enhancement; speech recognition; AURORA 2; DCT domain; SURE-MSE speech enhancement; Stein unbiased risk estimate; automatic speech recognition; denoising strategy; linear parametrization; noise minimization; noise whitening processing; robust speech recognition; speech thresholding coefficient; white Gaussian noise; Accuracy; Noise reduction; Signal to noise ratio; Speech; Speech enhancement; Speech recognition; MMSE; Speech enhancement; Stein´s unbiased risk estimate; automatic speech recognition;
Conference_Titel :
Chinese Spoken Language Processing (ISCSLP), 2010 7th International Symposium on
Conference_Location :
Tainan
Print_ISBN :
978-1-4244-6244-5
DOI :
10.1109/ISCSLP.2010.5684894