Comparison of HMM experts with MLP experts in the Full Combination Multi-Band Approach to Robust ASR
| Type of publication: | Conference paper |
| Citation: | astrid-00-21b |
| Booktitle: | ICSLP |
| Year: | 2000 |
| Note: | IDIAP-RR 00-21 |
| Crossref: | astrid-00-21a: |
| Abstract: | 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. |
| Userfields: | ipdmembership={speech}, language={English}, |
| Keywords: | full combination, HMM/ANN-Hybrid, multi-band, multi-stream |
| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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