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首頁> 外文期刊>IEEE Transactions on Signal Processing >Dynamic programming parsing for context-free grammars in continuous speech recognition
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Dynamic programming parsing for context-free grammars in continuous speech recognition

機譯：連續(xù)語音識別中無上下文語法的動態(tài)編程解析

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摘要

The use of context-free grammars in automatic speech recognition is discussed. A dynamic programming algorithm for recognizing and parsing spoken word strings of a context-free grammar is presented. The time alignment is incorporated in to the parsing algorithm. The algorithm performs all functions simultaneously, namely, time alignment, work boundary detection, recognition, and parsing. As a result, no postprocessing is required. From the probabilistic point of view, the algorithm finds the most likely explanation or derivation for the observed input string, which amounts to Viterbi scoring rather than Baum-Welch scoring in the case of regular or finite-state languages. The algorithm provides a closed-form solution. The computational complexity of the algorithm is studied. Details of the implementation and experimental tests are described.
機譯：討論了上下文無關(guān)語法在自動語音識別中的使用。提出了一種用于識別和解析上下文無關(guān)語法的口語字符串的動態(tài)編程算法。時間對齊被合并到解析算法中。該算法同時執(zhí)行所有功能，即時間對齊，工作邊界檢測，識別和解析。結(jié)果，不需要后處理。從概率的角度來看，該算法為觀察到的輸入字符串找到最可能的解釋或推導(dǎo)，這相當(dāng)于維特比評分而不是常規(guī)或有限狀態(tài)語言的Baum-Welch評分。該算法提供了一種封閉形式的解決方案。研究了算法的計算復(fù)雜度。詳細介紹了實現(xiàn)方式和實驗測試。

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