摘要
應用音節群組之加權型核心特徵矩陣於語音辨識替代錯誤修正
林立成* 吳宗憲**
國立成功大學資訊工程學系

近年來，語音辨識技術的成熟使其在科技領域的應用上蓬勃發展。然而，由於語音辨識技術在複雜環境等不同變數的影響之下，使其辨識率依然受到限制。所造成的語音辨識的錯誤輸出因此影響了語音辨識技術的實用價值。
因此，本論文透過後處理的方式，利用自然語言處理方式提出前後文相關之音節群組（Context Dependent Syllable Cluster、簡稱CDSC）為主的音節群組之加權型核心特徵矩陣，透過所提出此前後文相關之音節利用發音屬性進行分群，計算核心特徵矩陣結合音節混淆矩陣所得出的音節群組之加權型核心特徵矩陣，來找出最為相似性之音節，以用來改善語音辨識器輸出結果之辨識率。
利用本論文所提出之音節群組之加權型核心特徵矩陣，對語音辨識器之錯誤輸出結果進行修正，能分別將詞錯誤率由原本48.50%降低至45.31%及音節錯誤率由原本15.37%降低至10.31%。實驗結果說明了本論文所提出之方法，能有效地對語音辨識器所輸出的替代錯誤進行修正。

關鍵字－前後文相關之音節，語音辨識，錯誤修正，自然語言處理
*作者 **指導教授

Abstract
A Syllable Cluster Based Weighted Kernel Feature Matrix for ASR Substitution Error Correction
David Sarwono * Chung-Hsien Wu**
Institute of Computer Science and Information Engineering,
National Cheng Kung University, Tainan, Taiwan, R.O.C.

In recent years Automatic Speech Recognition (ASR) technology has become one of the most growing technologies in engineering science and research. However, the performance of ASR technology is still restricted in adverse environments. Errors in Automatic Speech Recognition outputs lead to low performance for speech applications, therefore correction techniques for these errors will be beneficial to applications relied on ASR outputs.
In this study, A Syllable Cluster Based Weighted Kernel Feature Matrix based on Context Dependent Syllable Cluster (CDSC) is proposed for the generation of correction candidates. For candidate selection in the second stage, the n-gram language model is used to determine the final corrected sentence output, thus to improve speech recognition output results recognition rate.
Experiments show that the proposed method improved from 48.50% to 45.31% and 15.37% to 10.31% in terms of Word Error Rate score and Syllable Error Rate as compared to the speech recognition approach.

Keyword－Context Dependent Syllable, Automatic Speech Recognizer, Error Correction, Natural Language Processing
* The Author ** The Advisor

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