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ESANN
2008

Word recognition and incremental learning based on neural associative memories and hidden Markov models

13 years 5 months ago
Word recognition and incremental learning based on neural associative memories and hidden Markov models
Abstract. An architecture for achieving word recognition and incremental learning of new words in a language processing system is presented. The architecture is based on neural associative memories and hidden Markov models. The hidden Markov models generate subword-unit transcriptions of the spoken words and provide them as input to the associative memory module. The associative memory module is a network of binary autoand heteroassociative memories and responsible for combining words from subword-units. The basic version of the system is implemented for simple command sentences. Its performance is compared with the performance of the hidden Markov models.
Zöhre Kara Kayikci, Günther Palm
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where ESANN
Authors Zöhre Kara Kayikci, Günther Palm
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