Comparison of HMM experts with MLP experts in the Full Combination Multi-Band Approach to Robust ASR

In this paper we apply the Full Combination (FC) multi-band approach, which has originally been introduced in the framework of posterior-based HMM/ANN (Hidden Markov Model/Artificial Neural Network) hybrid systems, to systems in which the ANN (or Multilayer Perceptron (MLP)) is itself replaced by a Multi Gaussian HMM (MGM). Both systems represent the most widely used statistical models for robust ASR (automatic speech recognition). It is shown how the FC formula for the likelihood-based MGMs can easily be derived from the posterior-based approach by simply applying Bayes' Rule. The experiments show that the Full Combination multi-band system with MGM experts performs better, in all noise conditions tested, than the simple sum and product rules which are normally used. As compared to the baseline full-band system, the FC system shows increased robustness mainly on band-limited noise. The goal of this article is not a performance comparison between Multilayer Perceptrons and Multi Gaussian Models but between the theory of the two approaches, posterior-based vs. likelihood-based FC approach, so results are only given for the MGMs.


Year:
2000
Publisher:
Martigny, Switzerland, IDIAP
Keywords:
Note:
Published: ICSLP 2000, Beijing, September 2000
Laboratories:




 Record created 2006-03-10, last modified 2018-01-27

External links:
Download fulltextURL
Download fulltextn/a
Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)