11th Annual Conference of the International Speech Communication Association

Makuhari, Chiba, Japan
September 26-30. 2010

Artificial and Online Acquired Noise Dictionaries for Noise Robust ASR

Jort F. Gemmeke (1), Tuomas Virtanen (2)

(1) Radboud Universiteit Nijmegen, The Netherlands
(2) Tampere University of Technology, Finland

Recent research has shown that speech can be sparsely represented using a dictionary of speech segments spanning multiple frames, emph{exemplars}, and that such a sparse representation can be recovered using Compressed Sensing techniques. In previous work we proposed a novel method for noise robust automatic speech recognition in which we modelled noisy speech as a sparse linear combination of speech and noise exemplars extracted from the training data. The weights of the speech exemplars were then used to provide noise robust HMM-state likelihoods. In this work we propose to acquire additional noise exemplars during decoding and the use of a noise dictionary which is artificially constructed. Experiments on AURORA-2 show that the artificial noise dictionary works better for noises not seen during training and that acquiring additional exemplars can improve recognition accuracy.

Full Paper

Bibliographic reference.  Gemmeke, Jort F. / Virtanen, Tuomas (2010): "Artificial and online acquired noise dictionaries for noise robust ASR", In INTERSPEECH-2010, 2082-2085.