Learning feature mapping using deep neural network bottleneck features for distant large vocabulary speech recognition
Ivan Himawan, Petr Motlícek, David Imseng, Blaise Potard, Namhoon Kim, Jaewon Lee
Abstract
Automatic speech recognition from distant microphones is a difficult task because recordings are affected by reverberation and background noise. First, the application of the deep neural network (DNN)/hidden Markov model (HMM) hybrid acoustic models for distant speech recognition task using AMI meeting corpus is investigated. This paper then proposes a feature transformation for removing reverberation and background noise artefacts from bottleneck features using DNN trained to learn the mapping between distant-talking speech features and close-talking speech bottleneck features. Experimental results on AMI meeting corpus reveal that the mismatch between close-talking and distant-talking conditions is largely reduced, with about 16% relative improvement over conventional bottleneck system (trained on close-talking speech). If the feature mapping is applied to close-talking speech, a minor degradation of 4% relative is observed.
BibTeX
@inproceedings{icassp2015_learningfeaturem,
title = {Learning feature mapping using deep neural network bottleneck features for distant large vocabulary speech recognition},
author = {Ivan Himawan and Petr Motlícek and David Imseng and Blaise Potard and Namhoon Kim and Jaewon Lee},
booktitle = {ICASSP 2015},
year = {2015}
}