Title :
Improved noise robustness by corrective and rival training
Author :
Meyer, Carsten ; Rose, Georg
Author_Institution :
Philips Res. Lab., Aachen, Germany
Abstract :
We show that discriminative training methods have the potential to improve noise robustness even for high resolution acoustic models trained on noisy data. To this end, we compare the performance of acoustic models trained on noisy data using maximum likelihood (ML), corrective (CT) and rival training (RT). Experiments are performed on a German and a Dutch continuous digit string recognition task, yielding improvements in the range of 12% to 35% relative
Keywords :
AWGN; acoustic noise; maximum likelihood estimation; noise pollution; speech recognition; AWGN; Dutch; German; acoustic model training; acoustic models; additive Gaussian white noise; additive real car noise; automatic speech recognition; continuous digit string recognition; corrective training; discriminative training; environmental noise; high resolution acoustic models; maximum likelihood training; noise robustness; noisy data; rival training; Acoustic noise; Automatic speech recognition; Laboratories; Noise robustness; Singular value decomposition; Speech enhancement; Testing; Training data; Wiener filter; Working environment noise;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on
Conference_Location :
Salt Lake City, UT
Print_ISBN :
0-7803-7041-4
DOI :
10.1109/ICASSP.2001.940825
