F0 feature extraction by polynomial regression function for monosyllabic Thai tone recognition

This paper presents a monosyllabic Thai tone recognition system, which is based on the Ant-Miner algorithm. The system is composed of three main processes, fundamental frequency (F0) extraction from input speech signal, analysis of F0 contour for feature extraction, In the F0 feature extraction, the polynomial regression functions are employed to fit the segmented F0 curve where its coefficients are used as a feature vector [8]. In tone recognition, we used the Ant-Miner classifier to classify a tone by assuming that the feature is features vector. The hypothetical words used in this paper are composed of numerical words and monosyllabic Thai words. The experimental results show that by using the system as a speaker-dependent system, the maximum recognition rate is 96.20%

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A comparison of front-end analyses for Thai speech recognition

Karnjanadecha¹,

Kimsawad²

2002

7th International Conference on Spoken Language Processing (ICSLP 2002)

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F0 feature extraction by polynomial regression function for monosyllabic Thai tone recognition

Cited by 3 publications

References 4 publications

Tone recognition of Thai continuous speech using Fujisaki's model

Tone recognition of Thai continuous speech using Fujisaki's model

Thai Tone Recognition Using Ant Colony Algorithm

A comparison of front-end analyses for Thai speech recognition

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