Investigation of phonemic context in speech using self-organizing feature maps

Some experiments with a neural-network model based on the self-organizing feature map algorithm are described. The main problem in phonemic recognition is the overlapping of feature vectors due to variability of speech and due to the coarticulation effect. This property of speech is reflected in the self-organized neural-network model in that a network unit can respond to more than one phonemic class. The authors have shown for their database that the sequence of responding units is consistent and similar for isolated utterances of the same word and distinct for different words. Thus, recognition can be based on network sequence identification. However, it is desirable that this sequence be somewhat simplified. Toward this goal they propose an algorithm for sequence smoothing. It is proposed that this network can be used as the feature extraction stage of another neural network that can learn the responding sequences as part of a speech recognition system.<
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