Tone Question of Tree Based Context Clustering for Hidden Markov Model Based Thai Speech Synthesis

2011

Problem statement: In tonal language speech; such as Thai, Mandarin and Vietnam, tone is an important feature of a syllable that must be taken into consideration, since tone is a supra-segmental feature in the speech prosody. Modeling of tone with high accuracy could enhance the quality of synthesized speech in the speech synthesis system. This study focuses on a model analysis of fundamental frequency (F0) contours of Thai tones using tone-geometrical model. Approach: Tonegeometrical model applied is this study is a basic model which is expected to extract the dimensional features of a syllable-length portion of fundamental frequency contour. Seven selected parameters are extracted from a syllable-length portion of fundamental frequency contour and then are analyzed. Results: In the experiments, 2,500 speech utterances from TSynC-1 speech database were selected and used as speech materials. The distributions for all seven parameters are presented. The statistical figures of mean and standard deviation values from five tones are also calculated. The results show that most of the proposed parameters can distinguish five Thai tones explicitly. Conclusion: From the finding, the proposed parameters of tone-geometrical model could be further applied in the speech or other speech processing technologies

“…The 2500 speech utterances from TSynC-1 speech corpus of NECTEC are used for model analyzing. (Chomphan, 2009;Alshamasin, 2009;Chomphan, 2010a;2010b;2010c;2010d;2010e).…”

Section: Resultsmentioning

confidence: 99%

Analytical Study on Fundamental Frequency Contours of Thai Tones using Tone-Geometrical Model

2011

“…The following analysis procedures are implemented for an utterance from the speech data material and noise data material (Abdellaoui, 2009;Chomphan, 2010b;2010c;2010d;2010e;Lampson et al, 2010;Ramadan, 2010;Teymourzadeh et al, 2010):…”

Section: Procedures Of Fundamental Frequency Contour Analysismentioning

confidence: 99%

Effects of Noises on the Analysis of Fundamental Frequency Contours for Thai Speech

2011

Problem statement:In speech communication, noises from surrounding environments affect the communication quality with various aspects. The received speech quality should be analyzed to see how the important noises reduce the speech quality so that we can eliminate them in an appropriate way. Approach: This study presents a study on the analysis of the noise effects on Thai speech. Four kinds of noises; air conditioner, car, factory and train, are chosen to be simulated in the study. The various levels of signal-to-noise ratios are conducted. The root mean square error between the fundamental frequency contours of the corrupted speech and the clean speech is calculated. Finally, the analysis of the root mean square error in terms of comparisons among genders, the four kinds of noises and various levels of signal-to-noise ratios is performed. Results: In the experiments, 400 speech utterances of male and female are used as speech materials. The average values of root mean square error are calculated. The results show that the fundamental frequency contour of female speech is affected more than that of male speech. Comparing among four kinds of noises, the car noise has the highest influence, while the factory noise has the lowest influence. Moreover, the root mean square error is inversely proportional to the level of signal-to-noise ratio. Conclusion: From the finding, the noises from surrounding environments have affected the speech quality of fundamental frequency contour. This study is the preliminary knowledge to enhance the speech quality for further works such as speech synthesis systems or other speech processing technologies.

“…This study could bring about an appropriate construction of question sets for the decision trees. In the other words, this study consequently aims at improving the synthetic speech quality (Chomphan, 2009;2010a;2010b).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Decision Trees in Context Clustering of Hidden Markov Model Based Thai Speech Synthesis

2011

Problem statement: In Thai speech synthesis using Hidden Markov model (HMM) based synthesis system, the tonal speech quality is degraded due to tone distortion. This major problem must be treated appropriately to preserve the tone characteristics of each syllable unit. Since tone brings about the intelligibility of the synthesized speech. It is needed to establish the tone questions and other phonetic questions in tree-based context clustering process accordingly. Approach: This study describes the analysis of questions in tree-based context clustering process of an HMM-based speech synthesis system for Thai language. In the system, spectrum, pitch or F0 and state duration are modeled simultaneously in a unified framework of HMM, their parameter distributions are clustered independently by using a decision-tree based context clustering technique. The contextual factors which affect spectrum, pitch and duration, i.e., part of speech, position and number of phones in a syllable, position and number of syllables in a word, position and number of words in a sentence, phone type and tone type, are taken into account for constructing the questions of the decision tree. All in all, thirteen sets of questions are analyzed in comparison. Results: In the experiment, we analyzed the decision trees by counting the number of questions in each node coming from those thirteen sets and by calculating the dominance score given to each question as the reciprocal of the distance from the root node to the question node. The highest number and dominance score are of the set of phonetic type, while the second, third highest ones are of the set of part of speech and tone type. Conclusion: By counting the number of questions in each node and calculating the dominance score, we can set the priority of each question set. All in all, the analysis results bring about further development of Thai speech synthesis with efficient context clustering process in an HMM-based speech synthesis system.