Robust distributed speech recognition using two-stage Filtered Minima Controlled Recursive Averaging
Negar Ghourchian, Sid‐Ahmed Selouani, Douglas D. O’Shaughnessy · 2009
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.