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

Abstract

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.