Enhancing Speech Features in Various Domains for Noise-Robust Speech Recognition

• Main Authors: Wen-Hsiang Tu, 杜文祥
• Other Authors: Jeih-weih Hung
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/66756772463135462510

博士 === 國立暨南國際大學 === 電機工程學系 === 100 === The performance of an automatic speech recognition (ASR) system is often degraded due to the various types of noise and interference in the application environment. In this disseration, we aim to develop robustness methods specifically for handling additive noise and channel disturbance. In particular, these developed methods are used to refine the mel-frequency cepstral coefficient (MFCC), which is one of the most widely used speech feature representation in ASR. At first, we discuss the effect of noise in the linear spectral domain of MFCC, and then present the approach of magnitude spectrum enhancement (MSE) to refine the spectrum of speech signals. Next, the method of hybrid cepstral statistics normalization is presented to process the MFCC in the mel-spectral domain. Finally, two novel compensation algorithms, modulation spectrum replacement (MSR) and modulation spectrum filtering (MSF), are provided to enhance the MFCC in the cepstral domain. The recognition experiments conducted on the Aurora-2 connected-digit database show that the aforementioned novel methods are capable of improving the recognition accuracy of the MFCC in various noise conditions, and in most cases they perform better than, or at least similarly to, the state-of-the-art noise robustness techniques such as Wiener filtering (WF), spectral subtraction (SS), mean and variance normalization (MVN) and histogram equalization (HEQ).