Classification of Speech Pattern Using Locally Recurrent Neural Networks

Subject of automatic speech recognition is the classification of speech pattern as phones, syllables or words. Speech is generated by a complex articulation process which is influenced by coarticulation effects and depends on speaker characteristics. Thus, static as well as dynamic aspects of the resulting speech signal must be captured during feature extraction. Optimum classification of speech pattern consisting of feature vectors can only be achieved if this representation of information is adequate for the chosen classification method. Here we present such a combination consisting of psychoacoustically oriented features and locally recurrent neural networks.