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|Title:||Gender identification from Thai speech signal using a neural network|
|Citation:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol.5863 LNCS, No.PART 1 (2009), 676-684|
|Abstract:||This paper proposes a method for identifying a gender by using a Thai spoken syllable with the Average Magnitude Difference Function (AMDF) and a neural network (NN). The AMDF is applied to extracting pitch contour from a syllable. Then the NN uses the pitch contour to identify a gender. Experiments are carried out to evaluate the effects of Thai tones and syllable parts on the gender classification performance. By using a whole syllable, the average correct classification rate of 98.5% is achieved. While using parts of a syllable, the first half part gives the highest accuracy of 99.5%, followed by the middle and the last parts with the accuracies of 96.5% and 95.5%, respectively. The results indicate that the proposed method using pitch contour from any tones of the first half of a Thai spoken syllable or a whole Thai spoken syllable with the NN is efficient for identifying a gender. © 2009 Springer-Verlag Berlin Heidelberg.|
|Appears in Collections:||Scopus 2006-2010|
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