On compensating the Mel-frequency cepstral coefficients for noisy speech recognition

This paper describes a novel noise-robust automatic speech recognition (ASR) front-end that employs a combination of Mel-filterbank output compensation and cumulative distribution mapping of cepstral coefficients with truncated Gaussian distribution. Recognition experiments on the Aurora II connected digits database reveal that the proposed front-end achieves an average digit recognition accuracy of 84.92% for a model set trained from clean speech data. Compared with the ETSI standard Mel-cepstral front-end, the proposed front-end is found to obtain a relative error rate reduction of around 61%. Moreover, the proposed front-end can provide comparable recognition accuracy with the ETSI advanced front-end, at less than half the computation load.

• Type: Conference
• Year: 2006
• Where: ACSC
• Authors: Eric H. C. Choi
• Keywords: ACSC 2006, Computer Science, Front-end, Recognition Accuracy, Standard Mel-cepstral Front-end