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In the last decades, a great deal of research has been devoted to extending our capacity of verbal communication with computers through automatic speech recognition (ASR). Although optimum performance can be reached when the speech signal is captured close to the speaker’s mouth, there are still obstacles to overcome in making reliable distant speech recognition (DSR) systems. The two major sources of degradation in DSR are distortions, such as additive noise and reverberation. This implies that speech enhancement techniques are typically required to achieve best possible signal quality. Different methodologies have been proposed in literature for environment robustness in speech recognition over the past two decades (Gong (1995); Hussain, Chetouani, Squartini, Bastari & Piazza (2007)). Two main classes can be identified (Li et al. (2009)). The first class encompasses the so called model-based techniques, which operate on the acoustic model to adapt or adjust its parameters so that the system fits better the distorted environment. The most popular of such techniques are multi-style training (Lippmann et al. (2003)), parallel model combination (PMC) (Gales & Young (2002)) and the vector Taylor series (VTS) model adaptation (Moreno (1996)). Although model-based techniques obtain excellent results, they require heavy modifications to the decoding stage and, in most cases, a greater computational burden. Conversely, the second class directly enhances the speech signal before it is presented to the recognizer, and show some significant advantages with respect to the previous class:

Multi-channel Feature Enhancement for Robust Speech Recognition