SPEAKER INDEPENDENT PHONEME RECOGNITION IN A CONTINUOUS SPEECH CONTEXT USING TIME DELAY FEED FORWARD NEURAL NETWORKS
In scaling the problem of automatic speech recognition down to a smaller sub problem that of recognizing individual utterances this exploration into a connectionist model of phoneme recognition stems from the assumption that phonemes should be identifiable as part of a surrounding context. By including contextual clues in the form of multiple contiguous spectral speech frames, a feedforward neural network trained through error back propagation will have more information from which to derive phonemic classifications. An additional assumption is that multiple speaker data is not only desirable, but even necessary for proper training of a robust recognition system.