利用加速演算法之大詞彙連續語音辨識系統

Abstract

本論文主要探討如何建構大詞彙連續語音辨識系統的加速演算法及其應用。第一部份先對語音辨識系統各部份的加速演算法做一系列的研究與實做分析，主要由聲學模型、語言模型和搜尋演算法三方面去做加速，而使得辨識率的下降在極小的範圍內。在Treebank語料實驗中，可減少50%以上辨識時間，其辨識率幾乎無衰退；在非特定語者TCC300語料的實驗中，辨識時間可降低22~44%左右，而辨識率只下降在1%範圍內。 其次，第二部份對於辨識系統建立不同的應用，設計出有文法規則的辨識系統，並且使該系統更具彈性。可藉由簡易的方式去調整系統的模型與參數，方便往後使用者去實做出不同需求的語音辨識系統。

This thesis can be divided into two parts. In the first part, large vocabulary continuous speech recognition (LVCSR) by speedup algorithms is constructed. The thesis describes some effective algorithms that reduce the computation of the acoustic model (AM) , language model (LM) and search space. In the outside of Treebank, the system recognition speed can be accelerated by more than 50%, and maintain the same recognition accuracy; besides, in the TCC300, the recognition speed also can be accelerated by more than 22%, and the character accuracy just decreases by less than 1%. Therefore the system is capable of the speaker independent recognition. In the second part of the thesis, a flexible LVCSR for the different applications is built. The user can not only tune up the system’s parameter and on line, but also be easy to design the grammar-ruled word net, and compile the language model which the recognition system can read in.