Abstract
The problem of automatic speech recognition in an adverse environment has attracted the attention of many researchers. The main reason is that the performance of existing speech recognition systems, whose designs are predicated on assumptions about the environment conditions, such as low noise or low interference, degrades rapidly in the presence of noise and distortion. In this paper, we review several promising methods that were proposed in the past few years to deal with this problem. We discuss methods or algorithms in six categories: signal enhancement preprocessing; special transducer arrangements; noise masking; stress compensation; robust distortion measures; and novel speech representations. We explain each type of approach and provide a summary of the performance improvements each method is able to achieve. This type of information is helpful in making a technical decision for the actual recognizer design to be deployed in adverse environments.
Published Version
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