調變頻譜正規化法使用於強健語音辨識之研究 (Study of Modulation Spectrum Normalization Techniques for Robust Speech Recognition) [In Chinese]

The performance of an automatic speech recognition system is often degraded due to the embedded noise in the processed speech signal. A variety of techniques have been proposed to deal with this problem, and one category of these techniques aims to normalize the temporal statistics of the speech features, which is the main direction of our proposed new approaches here. In this thesis, we propose a series of noise robustness approaches, all of which attempt to normalize the modulation spectrum of speech features. They include equi-ripple temporal filtering (ERTF), least-squares spectrum fitting (LSSF) and magnitude spectrum interpolation (MSI). With these approaches, the mismatch between the modulation spectra for clean and noise-corrupted speech features is reduced, and thus the resulting new features are expected to be more noise-robust. Recognition experiments implemented on Aurora-2 digit database show that the three new approaches effectively improve the recognition accuracy under a wide range of noise-corrupted environment. Moreover, it is also shown that they can be successfully combined with some other noise robustness approaches, like CMVN and MVA, to achieve a more excellent recognition performance. 關鍵詞:語音辨識、調變頻譜正規化、強健性語音特徵參數 keyword: speech recognition, modulation spectrum, robust speech features 一、緒論 自動語音辨識系統(automatic speech recognition systems, ASR),藉由多年來各方學者的 研究發展,逐漸達到實際應用的階段,而為人類生活帶來更多方便與幫助,雖然還不能 達到一個完美的地步,但是這方面的技術仍一直不斷地進步當中。 自動化語音辨認仍有許多相當具有挑戰性的研究課題,由於語音的變異性太多,例 如每位語者說話的方式與口氣都不一樣、不同語言有不同的特性、語者當時說話的情 緒、語者所處的環境是否有其他雜訊干擾等,這些變異對於語音辨識效果都有影響。在 真實應用環境下,語音辨識系統所遇到的主要問題其中兩個,分別為: (一)語者不匹配(speaker mismatch) 語者不匹配的問題是因為說話者先天條件(如口腔形狀)與後天習慣(如說話腔調) 的差異所產生的變異性,因此當以特定語者所訓練出來的聲學模型來辨識不屬於此特定 語者的語音時,辨識效果常會明顯下降,而要克服這一類問題的方法,通常是使用所謂 的語者調適(speaker adaptation)技術。也就是將原本訓練出來的聲學模型調適成接近當 下語者之語音特性的模型[1],如此便可提高辨識率。 (二)環境不匹配(environment mismatch) 環境不匹配的問題是因為語音辨識系統訓練環境與我們實驗或應用時的環境不同 所致,其變異因子主要包含了加成性雜訊(additive noise),如車站四周的雜訊、嘈雜街 道的人聲或車聲等,及摺積性雜訊(convolutional noise),如不同的有線或無線電話線路 或麥克風所造成的通道效應等,語音辨識系統常會因這些雜訊的影響使辨識率降低。下 圖一為乾淨語音受雜訊干擾之示意圖。