Noise reduction for noise robust feature extraction for distributed speech recognition
Bernhard Noé, Jürgen Sienel, Denis Jouvet, Laurent Mauuary, Johan de Veth, Louis Boves, Febe de Wet · 2001
This paper describes the noise robust feature extraction meth ods developed by France Telecom and Alcatel for the noise robust front-end standardisation of ETSI Aurora.It is shown that both noise reduction methods give a substantial im provement when compared to a standard MFCC feature ex traction algorithm for speech recognition in noisy environ ments.In addition, blind equalisation and feature vector se lection were used for further improvement of recognition performance.Results are discussed for the ETSI Aurora 2 task and the SDC-Italian task as well.It was found that the combi nation of noise reduction with the proposed methods is capa ble to achieve around 50% reduction of the error rate.In the context of the open ETSI Aurora standardisation, two propos als were submitted based on these methods, they achieved the best results among all the proposals.