Robust Speech Recognition for Adverse Environments
Chung‐Hsien Wu, Chao-Hong Liu · InTech eBooks · 2012
As the state-of-the-art speech recognizers can achieve a very high recognition rate for clean speech, the recognition performance generally degrades drastically under noisy environments. Noise-robust speech recognition has become an important task for speech recognition in adverse environments. Recent research on noise-robust speech recognition mostly focused on two directions: (1) removing the noise from the corrupted noisy signal in signal space or feature space such as noise filtering: spectral subtraction (Boll 1979), Wiener filtering (Macho et al. 2002) and RASTA filtering (Hermansky et al. 1994), and speech or feature enhancement using model-based approach: SPLICE (Deng et al. 2003) and stochastic vector mapping (Wu et al. 2002); (2) compensating the noise effect into acoustic models in model space so that the training environment can match the test environment such as PMC (Wu et al. 2004) or multi-condition/multi-style training (Deng et al. 2000). The noise filtering approaches require some assumption of prior information, such as the spectral characteristic of the noise. The performance will degrade when the noisy environment vary drastically or under unknown noise environment. Furthermore, (Deng et al. 2000; Deng et al. 2003) have shown that the use of denoising or preprocessing are superior to retraining the recognizers under the matched noise conditions with no preprocessing.