Fusion of Multiple Uncertainty Estimators and Propagators for Noise Robust ASR

Dung Tran 1 Emmanuel Vincent 1 Denis Jouvet 1
1 PAROLE - Analysis, perception and recognition of speech
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : Uncertainty decoding has been successfully used for speech recognition in highly nonstationary noise environments. Yet, accurate estimation of the uncertainty on the denoised signals and propagation to the features remain difficult. In this work, we propose to fuse the uncertainty estimates obtained from different uncertainty estimators and propagators by linear combination. The fusion coefficients are optimized by minimizing a measure of divergence with oracle estimates on development data. Using the Kullback-Leibler divergence, we obtain 18\% relative error rate reduction on the 2nd CHiME Challenge with respect to conventional decoding, that is about twice as much as the reduction achieved by the best single uncertainty estimator and propagator.
Document type :
Conference papers
Complete list of metadatas

https://hal.inria.fr/hal-00955185
Contributor : Dung Tran <>
Submitted on : Tuesday, March 4, 2014 - 9:21:01 AM
Last modification on : Thursday, March 21, 2019 - 2:20:42 PM
Long-term archiving on: Wednesday, June 4, 2014 - 10:50:35 AM

File

fusion_ICASSP2014.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00955185, version 1

Citation

Dung Tran, Emmanuel Vincent, Denis Jouvet. Fusion of Multiple Uncertainty Estimators and Propagators for Noise Robust ASR. 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2014, Florence, Italy. ⟨hal-00955185v1⟩

Share

Metrics

Record views

58

Files downloads

70