強健性語音辨識中能量相關特徵之改良式正規化技術的研究

Study of the Improved Normalization Techniques of Energy-Related Features for Robust Speech Recognition

隨著科技的發展，自動語音辨識技術也逐漸成熟，而達實際應用的階段；但當一自動語音辨識系統使用於現實環境中時，往往會受到雜訊的干擾，而造成辨識率大幅的下降；因此，環境相關的語音強健技術就顯得格外重要。本論文是針對在加成性雜訊所造成的辨識系統之訓練與辨識環境不匹配作為主要探討的課題，除了概述許多語音特徵之強健性處理技術外，主要重點在於介紹我們所新發展的能量相關特徵強健化演算法—靜音特徵正規化法。在此，我們以較嚴謹的數學分析，探討加成性雜訊對能量相關特徵造成的失真現象；接著根據這些現象，我們發展相對應的一套新技術，即靜音特徵正規化法，來降低這些失真。透過這一系列的辨識實驗，證實我們所提出的新技術能夠有效提升各種加成性雜訊環境下的語音辨識率，並與其它許多強健性技術有良好的加成性。

The rapid development of speech processing techniques has made themselves successfully applied in more and more applications, such as automatic dialing, voice-based information retrieval, and identity authentication. However, some unexpected variations in speech signals deteriorate the performance of a speech processing system, and thus relatively limit its application range. Among these variations, the environmental mismatch caused by the embedded noise in the speech signal is the major concern of this paper. In this paper, we provide a more rigorous mathematical analysis for the effects of the additive noise on two energy-related speech features, i.e. the logarithmic energy (logE) and the zeroth cepstral coefficient (c0). Then based on these effects, we propose a new feature compensation scheme, named silence feature normalization (SFN), in order to improve the noise robustness of the above two features for speech recognition. It is shown that, regardless of its simplicity in implementation, SFN brings about very significant improvement in noisy speech recognition, and it behaves better than many well-known feature normalization approaches. Furthermore, SFN can be easily integrated with other noise robustness techniques to achieve an even better recognition accuracy.