Noise-Robust Speech Recognition in a Car Environment Based on the Acoustic Features of Car Interior Noise

This paper describes an efficient method of improving the noise-robustness of speech recognition in a noisy car environment by considering the acoustic features of a car's interior noise. We analyzed the relationship between the Articulation Index values and the recognition rates in car environments under different driving conditions. We clarified that the recognition rate significantly worsens when the engine noise (periodic sound) components in the frequency range above 200 Hz were large. We developed a preprocessing method to improve the noiserobustness despite large amounts of engine noise. With this method, the cutoff frequency of the front-end high-pass filter is adaptively changed from 200 through 400 Hz according to the level of the engine noise components. The use of this method improved the average recognition rate for all eight cars under the second range acceleration condition by 11.9%, with the recognition rate for one of the cars being improved considerably by 38.6%.