Joint Estimation of Noise and Channel Distortion in a Generalized EM Framework

The performance of speech cleaning and noise adaptation

algorithms is heavily dependent on the quality of the noise

and channel models. Various strategies have been proposed

in the literature for adapting to the current noise and channel

conditions. In this paper, we describe the joint learning

of noise and channel distortion in a novel framework called

ALGONQUIN. The learning algorithm employs a generalized

EM strategy wherein the E step is approximate. We

discuss the characteristics of the new algorithm, with a focus

on convergence rates and parameter initialization. We

show that the learning algorithm can successfully disentangle

the non-linear effects of noise and linear effects of the

channel and achieve a relative reduction in WER of 21.8%

over the non-adaptive algorithm.

2001-trausti-asru.pdf
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In  IEEE Workshop on Automatic Speech Recognition and Understanding

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