Extending features for automatic speech recognition by means of auditory modelling

When investigating the benefit of auditory modelling for au- tomatic 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 het- eroscedastic discriminant analysis is introduced to summa- rize relevant information in feature spaces of lower dimen- sion 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 sta- tistically significant improvements are shown by combining standard MFCCs with the different features extracted from the auditory simulation model. The effect of different de- grees of regularization is investigated for this task.