A neural network for 500 word vocabulary word spotting using non-uniform units

We introduce acoustic sub-word units to neural networks for speaker-indepen dent continuous speech recognition. The functions of segmenting input and d etecting words are implemented with networks of simple structures. The non- uniform unit which we introduce in this research can model phoneme variatio ns caused by co-articulation spread over several phonemes and between words . These units can be segmented by the network according to stationary and t ransition parts of speech without iteration or without considering all poss ible position shifts. A word lexicon can be trained by the network, which c an effectively memorize all transcription variations in the training uttera nces of words. The results of speaker-independent word spotting of 520 word s with TIMIT data are described. (C) 2000 Elsevier Science Ltd. All rights reserved.