Two level continuous speech recognition using demisyllable-based HMM word spotting
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hdl:2117/111585
Tipus de documentText en actes de congrés
Data publicació1991
Condicions d'accésAccés obert
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
This paper describes a two level Spanish Continuous Speech Recognition System based on Demisyllable HMM modelling, word-spotting and finite-state lexical and syntactic knowledge. The first level, the word level, is based on a spotting algorithm which takes as input the unknown utterance, the HMM of the reference demisyllable and the lexical knowledge in terms of a finite-state network. The output of the word level is a lattice of word hypothesis [1]. The second level, the phrase level, searches in a time-synchronous procedure the best sentence that end at each time instant. It takes as input the word lattice and the syntactic knowledge in terms of a finite-state network, giving as output the best legal sentence. The proposal two-level system was tested recognizing the integers from 0 to 1000 in a speaker independent approach. We get a word accuracy of 93,2% with a sentence accuracy of 84. 5%. Keywords: Speech Recognition, Hidden Markov Model, Fuzzy Training, Demisyllable, Word-spotting, Multiple Hypothesis, Finite State Networks.
CitacióLleida, E., Mariño, J.B., Nadeu, C., Oliveras, A. Two level continuous speech recognition using demisyllable-based HMM word spotting. A: European Conference on Speech Communication and Technology (EUROSPEECH). "EUROSPEECH '91 - 2nd European Conference on Speech Communication and Technology: Genova, Italy: September 24-26, 1991". Genova: 1991, p. 1199-1202.
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