Robust distributed speech recognition using two-stage Filtered Minima Controlled Recursive Averaging

Abstract

This paper examines the use of a new Filtered Minima-Controlled Recursive Averaging (FMCRA) noise estimation technique as a robust front-end processing to improve the performance of a Distributed Speech Recognition (DSR) system in noisy environments. The noisy speech is enhanced by using a two-stage framework in order to simultaneously address the inefficiency of the Voice Activity Detector (VAD) and to remedy the inadequacies of MCRA. The performance evaluation carried out on the Aurora 2 task showed that the inclusion of FMCRA in the front-end side leads to a significant improvement in DSR accuracy.