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Improving ASR for continuous thai words using ANN/HMM

dc.contributor.authorSodanil, Maleerat
dc.contributor.authorNitsuwat, Supot
dc.contributor.authorHaruechaiyasak, Choochart
dc.contributor.editorEichler, Gerald
dc.contributor.editorKropf, Peter
dc.contributor.editorLechner, Ulrike
dc.contributor.editorMeesad, Phayung
dc.contributor.editorUnger, Herwig
dc.date.accessioned2019-01-11T09:33:33Z
dc.date.available2019-01-11T09:33:33Z
dc.date.issued2010
dc.description.abstractThe baseline system of an automatic speech recognition normally uses Mel- Frequency Cepstral Coefficients (MFCC) as feature vectors. However, for tonal language like Thai, tone information is one of the important features which can be used to improve the accuracy of recognition. This paper proposes a method for building an acoustic model for Thai-ASR using a combination of MFCC and tone information as an input feature vector. In addition, we apply Artificial Neural Network (ANN) multilayer perceptrons to estimate the posterior probabilities of a class model given a sequence of observation input. The performance of the ANN approach is compared with the Gaussian Mixture Model (GMM) used in the Hidden Markov Model Toolkit (HTK). The experiments were carried out with 2-grams and 3-grams of language model. The training and test data sets were prepared from reading speech of ten Aesop's stories from 5 male and 5 female speakers. The results showed that the proposed method can be used to improve the performance of Thai-ASR in term of reducing word error rate.en
dc.identifier.isbn978-3-88579-259-8
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/19020
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartof10th International Conferenceon Innovative Internet Community Systems (I2CS) – Jubilee Edition 2010 –
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-165
dc.titleImproving ASR for continuous thai words using ANN/HMMen
dc.typeText/Conference Paper
gi.citation.endPage256
gi.citation.publisherPlaceBonn
gi.citation.startPage247
gi.conference.dateJune 3-5, 2010
gi.conference.locationBangkok, Thailand
gi.conference.sessiontitleRegular Research Papers

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