Robust features derived from temporal trajectory filtering for speech recognition under the corruption of additive and convolutional noises

This paper presents a novel method using robust features for speech recognition when the speech signal is corrupted by additive and convolutional noises. This method is conceptually simple and easy to be implemented. The additive noise and the convolutional noise are removed by temporal trajectory filtering in the autocorrelation domain and cepstral domain, respectively. No prior information of noise corruption is necessary. A task of multi-speaker isolated digit recognition is conducted to demonstrate the effectiveness of using these robust features. The cases of the channel filtered speech signal corrupted by additive white noise and color noise are tested. Experimental results show that significant improvements can be achieved as compared with some traditional features