Robust Mandarin Speech Recognition for Car Navigation Interface

This paper presents a robust automatic speech recognition (ASR) system as multimedia interface for car navigation. In front-end, we use the minimum-mean square error (MMSE) enhancement to suppress the background in-car noise and then compensate the spectrum components distorted by noise over-reduction by smoothing technologies. In acoustic model training, an immunity learning scheme is adopted, in which pre-recorded car noises are artificially added to clean training utterances to imitate the in-car environment. The immunity scheme makes the system robust to both residual noise and speech enhancement distortion. In the context of Mandarin speech recognition, a special issue is the diversification of Chinese dialects, i.e. the pronunciation difference among accents decreases the recognition performance if the acoustic models are trained with an unmatched accented database. We propose to train the models with multiple accented Mandarin databases to solve this problem. The efficiency of the proposed ASR system is confirmed in evaluations.