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ROBUST SPEECH RECOGNITION IN THE AUTOMOBILE



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ROBUST SPEECH RECOGNITION IN THE AUTOMOBILE Nobutoshi Hanai and Richard M Stern Department of Electrical and Computer Engineering and School of Computer Science Carnegie Mellon University Pittsburgh PA 15213 ABSTRACT 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 shield wipers In this paper we consider the effects of all of these sources of degradation and we compare the extent to which these effects are ameliorated by the compensation techniques of cepstral mean normalization CMN 6 and codeword dependent cepstral normalization CDCN 7 2 DATABASES The experimental results in this paper were obtained by training the CMU SPHINX I system 8 on the previously described census database 7 and tested using a database of speech recorded in automobiles recorded by and provided by the Motorola Corporation In this section we describe the Motorola automotive database which was used to evaluate effects of the noise in the automobile on the SPHINX system We also briefly review the contents of the census database 2 1 The Motorola



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