When a speech recognition system is deployed outside the laboratory setting, it needs to handle many signal variabilities. These may be due to many factors, including additive noise, acoustic echo, and speaker accent. If the speech recognition accuracy doesn't degrade very much under these conditions, the system is called robust. Even though there are several reasons why real-world speech may differ from clean speech, in this chapter we focus on the influence of the acoustical environment, defined as the transformations that affect the speech signal from the time it leaves the mouth until it is in digital format.

Specifically, we discuss strategies for dealing with additive noise. Some of the techniques, like feature normalization, are general enough to provide robustness against several forms of signal degradation. Others, such as feature enhancement, provide superior noise robustness at the expense of being less general. A good system will implement several techniques, to provide a strong defense against acoustical variabilities.

Publisher: Springer Verlag
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