臺灣博碩士論文加值系統

韻律表現是影響語音自然度的重要因素，而音高更蘊含了豐富的韻律訊息。基於隱藏式馬可夫模型的語音合成器，近年來已可合成出流暢及可理解的語音，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需改善。因此，本研究以“階層式音韻架構”作為音高預測的基礎，各層的音韻單元考慮“動態參數特性”；希望改善傳統音韻模型以小單元合成的不足，並以動態參數保留時間上的關聯性，使單元之間的連接更加自然，藉以改善基於隱藏式馬可夫模型之合成語音的自然度。
在本論文中，對於應用階層式韻律架構及動態參數之音高預測模型，分為下列四項研究重點：(1)階層式韻律架構的預測及產生；(2)導入動態參數生成演算法於各韻律階層；(3)運用分類回歸樹及隱藏式馬可夫模型建立各層韻律模型；(4)參數提取使用STRAIGHT（Speech Transformation and Representation based on Adaptive Interpolation of weiGHTed spectrogram）。
在實驗中，首先對韻律停頓預測模型評估其預測準確度，接著對音高預測模型進行主觀及客觀的評估，證明本論文提出之方法，在合成語音的自然度表現上，有不錯的表現及改善。

Prosody is the main measurement of naturalness for speech, and pitch is the key factor known to carry the prosodic information. In resent years, speech synthesis based on Hidden Markov Models has been developed, which can synthesize smooth speech and in an advantageous position about its flexible property and portable in size. Nevertheless, there is still room for improvement in “the naturalness” of synthesized speech. In our research, we take the “prosody hierarchy structure” as the basis of pitch prediction model, and apply “dynamic features” to the unit of each hierarchical layer. We describe prosodic units as the supra-segmental units which occur in a hierarchy structure and reflect how brain processes speech; the latter preserve time correlation between adjacent units and result in more natural connection among each conjunction point. Applying this framework to HMM-based speech synthesis system, we can result a better, natural sounding speech.
The purpose of this thesis is to develop a pitch prediction model using prosody hierarchy structure and dynamic features and to investigate the improvement of naturalness for synthesized speech. More specifically, this research is aimed to: (1) Prediction and generation of prosody hierarchy structure; (2) Dynamic features for each hierarchical layer; (3) Building the pitch prediction model for each layer: CART for prosodic word and syllable level, HMM for frame level; (4) Feature analysis using STRAIGHT (Speech Transformation and Representation based on Adaptive Interpolation of weiGHTed spectrogram).
The experimental result using both subjective and objective tests in the proposed approach and other comparative systems shows that our scheme is better can comparative ones and can generate more natural sounding speech.

中文摘要.............................................................................................................................IV
Abstract................................................................................................................................ V
誌謝................................................................................................................................VI
圖目錄................................................................................................................................IX
表目錄................................................................................................................................XI
1 緒論 1
1.1 前言 1
1.1.1 研究背景 1
1.1.2 研究動機與目的 1
1.1.3 文獻回顧 2
1.2 研究方法簡介 4
1.2.1 系統架構 4
1.3 章節概要 7
2 HMM-based中文語音合成器 8
2.1 HMM-based語音合成系統 8
2.2 中文HMM模型之建立 10
2.2.1 中文音素模型 10
2.2.2 文字分析前處理器 13
2.2.3 狀態合併分裂樹（決策樹）之問題集 14
2.3 參數提取: STRAIGHT 15
3 韻律階層架構 17
3.1 中文韻律結構 17
3.2 韻律結構之產生 20
3.2.1 韻律結構預測模型 20
3.2.2 預測模型之建立 22
4 階層式音高模型 25
4.1 音高階層式結構 25
4.2 各層之音高量化 30
4.2.1 量化模型 31
4.2.2 音韻詞階層 32
4.2.3 音節階層 33
4.2.4 音框階層 34
4.3 各層音高模型之建立 34
4.3.1 動態參數 34
4.3.2 音韻詞/音節階層 37
4.3.3 音框階層 42
4.4 參數生成演算法 42
5 實驗結果與分析 44
5.1 實驗語料 44
5.1.1 北京清華大學語料庫 44
5.1.2 語料設定 45
5.2 實驗與評估 46
5.2.1 音韻停頓預測模型評估 46
5.2.2 音高預測模型評估 48
5.2.2.1 權重值設定 48
5.2.2.2 音高軌跡比較 55
5.2.2.3 客觀/主觀評估 57
5.2.2.4 分析與討論 60
6 結論與未來展望 62
6.1 結論 62
6.2 未來展望 62
參考文獻 64
0 附錄 68
作者簡歷 76

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[謝, 民63年] 謝雲飛, 語音學大綱, 民國63年初版