There are tonal languages around the world such as Red Indian in North and South America, Swedish and Norwegian in Europe, Bantu, Myanmar and Thai in Asia. The Thai language has four tone marks and five tonemes, the low, the falling, the mid, the high and the rising. The advantage of having tones is the existence of many words; different tones give the words different meanings. So it is necessary to utter the word with the tone correctly. This project was created with the purpose of studying the tone recognition by using F0 contours as the indicators, and advancing the Thai tone practicing program.
key words: tone, tone recognition, F0 contour, average F0 contour The program can be separated into 2 parts, Thai tone practicing for common persons (especially the foreigners) and Thai tone practicing for the hearing deficient persons. The program uses the tone recognition for checking the users’ utterances. When the voice of a program user is inputted, the fundamental frequency of the voice will be analyzed by the program. Then, the program will calculate the coefficients in the polynomial equations that fit the F0 contour. After we have the coefficients in matrix [B], the program will calculate the standard deviation, Baverage and Z-score of [B]. Outputs of the program are the user’s F0 contour compared with the average F0 model, tonal accuracy and degree of loudness.
Thai is a tonal language. There are five tonemes in Thai, the mid, the low, the falling, the high and the rising. The feature of spee ch, u sed to classify the ton e, is the shape of fundamental frequency (F0) contour, shown in Figure 1.
Figure 1 : Average F0 contours of the five Thai tones produced in isolation by a male speaker .
This program is efficient in a quiet place and the words which are used in practicing are monosyllabic and have long vowels. If the program user is not the congenital deaf, he would have a greater probability to succeed following the method of the program.
We will ask for cooperation from 50 MWIT students (25 males and 25 females) to record their voices. Each student records 15 voice clips, 3 clips per each Thai tone. While we are recording the voices, Thai teacher of MWIT advises the students to utter Thai tones correctly.
The system for monosyllabic Thai tone recognition has been proposed in this paper . We used the coefficient of polynomial regression function as a feature vector of the segmented F 0 contour. In the training phase, we will separate gender and tones into each case. After solving matrix equations we will have many of matrix B. Then, use fundamental statistics to find , S.D. and Z-score. The program will check Z-score of input with H0 by using the critical value in Gaussian Distribution.
Tabtong N. and Kitsirikul B., Support Vector Machines for Thai Phoneme Recognition , Machine Intelligence & Knowledge Discovery Laboratory Department of Computer Engineering, Chulalongkorn University Bangkok, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems : Vol. 9, No. 6 (2001) 803—813.
Wattanapong P. and Sukanek S., Continuous Thai Tone Recognition using Tri-Half-Tone Hidden Markov Model ( 2547 ), Available online: https://pindex.ku.ac.th/file_research/g 4365034 kufair.pdf
Least square method ( 2002 ), Available online: labnet.sci.ku.ac.th/course_web/computa/4_3LeastSq-2.doc.
Jittapankul S., F0 Feature Extraction by Polynomial Regression Function for Monosyllabic Thai Tone Recognition, Digital Signal Processing Research Laboratory, Department of Electrical Engineering,