Extending features for automatic speech recognition by means of auditory modelling
Gero Szepannek, Tamás Harczos, Frank Klefenz, Claus Weihs · 2009
When investigating the benefit of auditory modelling for automatic speech recognition applications typically different features or auditory simulation models are compared. In this work the attempt of combining several auditory model based feature extraction schemes is pursued, as well as their further combination with standard MFCC features. For this purpose a regularization of the common heteroscedastic discriminant analysis is introduced to summarize relevant information in feature spaces of lower dimension and uncorrelated single features. Besides standard auditory model- based features also new features are included that rely on delay computing networks to extract relevant information from the shape of the cochlear travelling wave delay trajectory. In an empirical study statistically significant improvements are shown by combining standard MFCCs with the different features extracted from the auditory simulation model. The effect of different degrees of regularization is investigated for this task. 1.