Robustifying cepstral features by mitigating the outlier effect for noisy speech recognition

The performance of automatic speech recognition (ASR) systems is often seriously degraded by noise interference. Among the techniques to reduce the noise effect, cepstral mean-and-variance normalization (CMVN) is a simple yet quite effective approach for processing MFCC speech features. However, the features processed by CMVN contain a significant number of outliers, which very likely weakens the effect of CMVN. This paper primarily proposes to deal with the outliers left by CMVN with two directions. The first one is to apply a sigmoid function transformation, which provides explicit lower and upper bounds for the outliers, and the second one exploits the well-known median filter to remove the impulse-like outliers in the CMVN features. Under the Aurora-2 digit recognition database and task, the presented two frameworks give rise to around 5% in absolute accuracy improvement in comparison with CMVN, and the corresponding word error rate reduction relative to the MFCC baseline is as high as 50%.