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Anocha Rugchatjaroen

Researcher at Thailand National Science and Technology Development Agency

Publications -  21
Citations -  88

Anocha Rugchatjaroen is an academic researcher from Thailand National Science and Technology Development Agency. The author has contributed to research in topics: Convolutional neural network & Radial basis function network. The author has an hindex of 4, co-authored 21 publications receiving 62 citations. Previous affiliations of Anocha Rugchatjaroen include University of York & Chulalongkorn University.

Papers
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Proceedings ArticleDOI

A Light-Weight Artificial Neural Network for Speech Emotion Recognition using Average Values of MFCCs and Their Derivatives

TL;DR: A novel approach to create a useful set of features for improving speech emotion recognition (SER) system by concatenating average values of MFCCs that are concatenated with delta and delta-delta coefficients to reduce the number of parameters and computational burden.
Proceedings ArticleDOI

A learning method for Thai phonetization of English words.

TL;DR: The proposed model is totally data-driven, starting by automatic grapheme-phoneme alignment, modeling transduction rules and predicting Thai syllabictones using learning machines and achieves acceptable results in both objective and text-tospeech synthesis subjective tests.
Proceedings ArticleDOI

T-tilt: a modified tilt model for F0 analysis and synthesis in tonal languages.

TL;DR: A modified Tilt model for analyzing and synthesizing F0 contours in tonal languages, called T-Tilt, is proposed, which requires extensive work on parameter synthesis although the synthesizing performance is comparable to those produced by other proposed models.
Proceedings ArticleDOI

The First Wikipedia Questions and Factoid Answers Corpus in the Thai Language

TL;DR: A Thai questions-answers corpus for a question-ANSwering task which was extracted from Thai Wikipedia which was downloaded on 17 December 2017 and comprises 5,000 annotated factoids.
Journal ArticleDOI

Efficient two-stage processing for joint sequence model-based Thai grapheme-to-phoneme conversion

TL;DR: A novel two-stage processing for Thai G2P is introduced, with as much as 14.49% absolute improvement from a baseline model using Context Free Grammar syllabification and syllable n-gram modeling.