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ON DNN POSTERIOR PROBABILITY COMBINATION IN MULTI-STREAM SPEECH RECOGNITION FOR REVERBERANT ENVIRONMENTS

Citation Author(s):
Feifei Xiong, Stefan Goetze, Bernd T. Meyer
Submitted by:
Feifei Xiong
Last updated:
28 February 2017 - 2:30am
Document Type:
Poster
Document Year:
2017
Event:
Presenters:
Feifei Xiong
Paper Code:
SP-P4.9
 

A multi-stream framework with deep neural network (DNN) classifiers has been applied in this paper to improve automatic speech recognition (ASR) performance in environments with different reverberation characteristics. We propose a room parameter estimation model to determine the stream weights for DNN posterior probability combination with the aim of obtaining reliable log-likelihoods for decoding. The model is implemented by training a multi-layer
perceptron to distinguish between various reverberant environments. The method is tested in known and unknown environments against approaches based on inverse entropy and autoencoders, with average relative word error rate improvements of 46% and 29%, respectively, when performing multi-stream ASR in different reverberant situations.

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