%Aigaion2 BibTeX export from Idiap Publications %Thursday 07 November 2024 06:58:35 AM @INPROCEEDINGS{astrid-00-21b, author = {Hagen, Astrid and Morris, Andrew}, keywords = {full combination, HMM/ANN-Hybrid, multi-band, multi-stream}, projects = {Idiap}, title = {Comparison of HMM experts with MLP experts in the Full Combination Multi-Band Approach to Robust ASR}, 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.}, pdf = {https://publications.idiap.ch/attachments/reports/2000/rr00-21.pdf}, postscript = {ftp://ftp.idiap.ch/pub/reports/2000/rr00-21.ps.gz}, ipdmembership={speech}, language={English}, } crossreferenced publications: @TECHREPORT{astrid-00-21a, author = {Hagen, Astrid and Morris, Andrew}, keywords = {full combination, HMM/ANN-Hybrid, multi-band, multi-stream}, projects = {Idiap}, month = {7}, title = {Comparison of HMM experts with MLP experts in the Full Combination Multi-Band Approach to Robust ASR}, type = {Idiap-RR}, number = {Idiap-RR-21-2000}, year = {2000}, institution = {IDIAP}, address = {Martigny, Switzerland}, note = {Published: ICSLP 2000, Beijing, September 2000}, 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.}, pdf = {https://publications.idiap.ch/attachments/reports/2000/rr00-21.pdf}, postscript = {ftp://ftp.idiap.ch/pub/reports/2000/rr00-21.ps.gz}, ipdinar={2000}, ipdmembership={speech}, language={English}, }