自然語音中發音變異之現象是影響語音自然度的重要因素。基於隱藏式馬可夫模型的語音合成器，近年來已經可合成出流暢及清晰的語音，其系統的可攜性及適應性更是其發展優勢，但在語音的自然度上仍嫌不足，需要改善合成語音的自然度。因此，本研究以“轉換函式”作為轉換並合成出發音變異現象的方法，以及考慮“構音特性參數”做發音變異現象之預測；透過轉換函式產生新的音韻模型，希望改善在傳統合成方法中，僅利用固定數量音韻模型合成的不足，並以構音特性參數對發音變異做聲學特性上的分類，以彌補訓練語料不足的問題。藉由產生發音變異現象，用以增進基於隱藏式馬可夫模型之合成語音的自然度。
在本論文中，對於應用轉換函式及發音變異預測模型，分為下列兩項研究重點：1）導入轉換函式於隱藏式馬可夫模型建立發音變異模型；2）運用分類迴歸樹預測發音變異種類。
對於發音變異模型進行主觀及客觀評估之實驗結果顯示，本論文所提出之方法，在合成語音之自然度表現上，具有相當程度的改進。

Pronunciation variation plays an important role in spontaneous speech. In recent years, speech synthesis based on Hidden Markov Models has been developed, which can synthesize smooth speech and clear pronunciation. The advantage of HMM-based synthesized system is its flexibility and portability. However, the “naturalness” of traditional HMM-based synthesized speech is not spontaneous. In our approach, the transformation function is adopted for the pronunciation variation modeling, and articulatory features are also considered to predict pronunciation variation. Transformation function is used to generate variation phones, to solve the problem of lacking of phone models in previous works. The Lack of training data is solved by clustering acoustic variations using articulatory features. In this work, we achieve the goal of synthesizing spontaneous speech by generating pronunciation variations.
This research aims to: 1) importing transformation function into HMM to model pronunciation variations, and 2) predicting pronunciation using Classification and Regression Tree.
Objective and subjective tests were used to evaluate the performance of our approach. The experimental results showed that the proposed method achieved a significant improvement in synthesized speech in terms of spontaneity.

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