以特徵參數正規化為基礎之強健性語音辨認

Abstract

在本論文中，主要是針對強健性語音特徵參數作深入的探討，將現有的倒頻譜正規化法及分佈等化法做些許的改進。我們將分佈等化法加上ARMA濾波器後，經由國語數字串辨認實驗，辨識率從80.08%提升到82.03%。另外，我們提出的分兩群式MVA系統，也在經過改良後，辨識率由傳統MVA系統的81.31%提升到82.26%，同時，我們也做了理想分群MVA系統實驗，得知若準確分群，辨識率可提升至83.63%。最後，我們利用正確的基頻將語音再多分一群，理想三群式MVA系統實驗結果顯示，辨識率可達86.25%。

In this thesis. Some robust speech feature processing algorithms were proposed, in order to improve the speech recognition performance under the noisy environments . First, the well-known robust speech feature processing algorithms such as mean variance normalization(MVN) and histogram equalization(HEQ) was implemented in a Mandarin AURORA-like system database as the base-line system. Then, the class-based MVA was proposed to further implement the speech recognition performance. The class-based MVA algorithm was first categorized the signal into speech and non-speech parts and applied MVAs to each class separately. A 82.26% recognition rate can be achieved comparing to 81.31% in traditional MVA. Final, a Three-class voiced, unvoiced and non-speech MVA was investigated. A 86.25% recognition rate can be achieved under the ideal category of voiced/unvoiced/non-speech case.