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Automatic Speech Recognition on Spontaneous Interview Speech

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dc.contributor.author Naing, Hay Mar Soe
dc.contributor.author Pa, Win Pa
dc.date.accessioned 2019-07-03T08:15:01Z
dc.date.available 2019-07-03T08:15:01Z
dc.date.issued 2018-02-22
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/329
dc.description.abstract This paper presents a spontaneous speech recognition system for Myanmar language. Automatic speech recognition (ASR) on some controlled speech has achieved almost human performance. However, the performance of spontaneous speech is drastically decreased due to the diversity of speaking styles, speak rate, presence of additive and non-linear distortion, accents and weakened articulation. In this study, we built a recognizer for Myanmar Interview speech by using the classical Gaussian Mixture Model based Hidden Markov Model (HMM-GMM) approach. We invested that the effect of variation on acoustic feature and number of senones and Gaussian densities on Myanmar Interview speech. According to these experiments, we achieved the best Word Error Rate (WER) of 20.47%. en_US
dc.language.iso en en_US
dc.publisher Sixteenth International Conferences on Computer Applications(ICCA 2018) en_US
dc.subject Spontaneous speech en_US
dc.subject ASR en_US
dc.subject HMM-GMM Myanmar en_US
dc.title Automatic Speech Recognition on Spontaneous Interview Speech en_US
dc.type Article en_US


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