Missing data ASR with fusion of features and combination of recognizers
Neil Joshi, Ling Guan · 2006
Speech recognition under noisy conditions has been actively researched and effective techniques have been developed to handle stationary noise. Under circumstances where the stationary assumption is not valid, the performance of speech recognizers is extremely poor. Missing data theory provides a method for the development of robust speech recognition under any noisy condition. A limitation to ASR with missing data theory techniques is the choice of features used in the model. There exist alternative feature representations that have been demonstrated to be much more effective for signal recognition purposes. This paper presents a novel method to incorporate the use of alternative feature sets within the realm of ASR with missing data theory techniques. Using the proposed combination of recognizers, or fusion of features, an ASR decoding process is developed based upon the coupling of spectral features using missing data techniques and traditional MFCC based features. The proposed technique is demonstrated to increase recognition performance under all experimented noise conditions over traditional missing data techniques.