Robust speech recognition in the automobile

In this paper we discuss a number of the ways in which the recognition accuracy of automatic speech recognition systems is affected by ambient noise in the automobile, along with the extent to which various techniques for robust speech recognition can provide for more robust recognition. We consider separately the effects of engine noise, interference by turbulent air outside the car, interference by sounds from the car’s radio, and interference by the sounds of the car’s windshield wipers. Recognition accuracy was compared using baseline processing, cepstral mean normalization (CMN), and codeword-dependent cepstral normalization (CDCN). The greatest degradation in recognition accuracy was produced by interference from AM-radio talk shows. The use of CMN and especially CDCN was found to be significantly improve recognition accuracy, except for the effects of interference from radio talk shows at low car speeds. This type of interference is effectively suppressed through the use of adaptive noise cancellation techniques.