A modified cascaded neuro-computational model applied to recognition of connected spoken Japanese prefecture words
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In this paper, a novel approach of connected spoken word recognition is proposed, based only on a relatively simple artificial neural network model. The model used is a modified version of the previously proposed cascaded neuro-computational model and has a three-layered network structure, where a non-linear metric to each of the second-layer units is newly introduced for performing effectively the pattern matching at the word-feature level. Simulations were conducted using connected speech data sets of a larger lexicon than those used in the previous works; the data sets were comprised of the naturally spoken strings, each string consisting of a varying number of 2–7 words selected from a total of 47 Japanese prefecture names. The simulation results show that the modified model yields the overall recognition performance, i.e., 95.2% in terms of the word accuracy rate, which is comparable to that (98.1%) obtained using a benchmark approach of hidden Markov model with embedded training.
KeywordsComputational linguistics Connected word recognition Connectionist model Natural language processing Neural networks Speech recognition
The author would like to thank all the students who participated in the recording sessions and Mr. Hideki Shimizu for his partial involvement in the simulation study for this work.
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