精確的標音對於以大語料為基礎的語音合成系統(corpus-based TTS)相當重要，然而以維特比(Viterbi)進行強制對位(forced alignment)的自動切音結果並不夠精確，加上適合某種語言的自動標音方式並不完全可以套用在另一種語言，因此，我們針對華語語料提供一種新的分界點微調(boundary refinement )方式。
本論文所使用的方法，針對華語的語音特性分成四大類，接著針對不同的分界點組合，我們利用圖形識別(pattern recognition)的方式選擇合適的聲學特徵，各自進行分界點微調，其中連音類(“periodic voiced + periodic voiced”)的分界點微調結果並不理想，關於此類我們採用以共振峰(formant)為基礎的新特徵進行特別處理。
為了驗證我們所提出方法的可行性，我們比較前人以CART(Classification and Regression Tree)為基礎的分界點微調方式，並提供許多實驗數據比較，根據實驗結果，我們所使用的分界點微調方式能夠得到相當高的切音準確率。

Precise phone/syllable boundary labeling of the utterances in a speech corpus plays an important role in constructing a corpus-based TTS (text-to-speech) system. However, automatic labeling based on Viterbi forced alignment does not always produce satisfactory results. Moreover, a suitable labeling method for one language does not necessarily produce desirable results for another language. Hence in this thesis, we propose a new procedure for refining the boundaries of utterances in a Mandarin speech corpus. This procedure employs different sets of acoustic features for four different phonetic categories. In addition, a new scheme is proposed to deal with the “periodic voiced + periodic voiced” case, which produced most of the segmentation errors in our experiment. Several experiments were conducted to demonstrate the feasibility of the proposed approach.

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