MULTI-FRAME GMM-BASED BLOCK QUANTISATION FOR DISTRIBUTED SPEECH RECOGNITION UNDER NOISY CONDITIONS

In this paper, we report on the recognition accuracy of the multi-frame GMM-based block quantiser for the coding of MFCC fea-tures in a distributed speech recognition framework under vary-ing noise conditions. All experiments were performed using the ETSI Aurora-2 connected-digits recognition task. For compari-son, we have also investigated other quantisation schemes such as the memoryless GMM-based block quantiser, the unconstrained vector quantiser, and non-uniform scalar quantisers. The results show that the rate-distortion effi ciency of the quantiser is a factor in determining the level of recognition accuracy at low to medium levels of additive noise. For high levels of additive noise, the in-fluence of rate-distortion effi ciency diminishes and the recognition accuracy becomes dependent on the recognition features. 1