Estimating room acoustic parameters for speech recognizer adaptation and combination in reverberant environments

This work analyzes the influence of reverberation on automatic speech recognition (ASR) systems and how to compensate its influence, with special focus on the important acoustical parameters i.e. room reverberation time T60 and clarity index C50. A multilayer perceptron (MLP) using features of a spectro-temporal filter bank as input is employed to identify the acoustic conditions spanning various reverberant scenarios. The posterior probabilities of the MLP are used to design a novel selection scheme for adaptation in a cluster-based manner and for system combination achieved by recognizer output voting error reduction (ROVER). A comparison of word error rates is performed considering different training modes, and an average relative improvement of 7.1% is obtained by the proposed system compared to conventional multistyle training