Histogram Equalization with Bayesian Estimation for Noise Robust Speech Recognition

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 158
  • Download : 0
The histogram equalization approach is an efficient feature normalization technique for noise robust automatic speech recognition. However, it suffers from performance degradation when some fundamental conditions are not satisfied in the test environment. To remedy these limitations of the original histogram equalization methods, class-based histogram equalization approach has been proposed. Although this approach showed substantial performance improvement under noise environments, it still suffers from performance degradation due to the overfitting problem when test data are insufficient. To address this issue, the proposed histogram equalization technique employs the Bayesian estimation method in the test cumulative distribution function estimation. It was reported in a previous study conducted on the Aurora-4 task that the proposed approach provided substantial performance gains in speech recognition systems based on the acoustic modeling of the Gaussian mixture model-hidden Markov model. In this work, the proposed approach was examined in speech recognition systems with deep neural network-hidden Markov model (DNN-HMM), the current mainstream speech recognition approach where it also showed meaningful performance improvement over the conventional maximum likelihood estimation-based method. The fusion of the proposed features with the mel-frequency cepstral coefficients provided additional performance gains in DNN-HMM systems, which otherwise suffer from performance degradation in the clean test condition.
Publisher
ACOUSTICAL SOC AMER AMER INST PHYSICS
Issue Date
2018-02
Language
English
Article Type
Article
Citation

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, v.143, no.2, pp.677 - 685

ISSN
0001-4966
DOI
10.1121/1.5022800
URI
http://hdl.handle.net/10203/240625
Appears in Collection
EE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0