Acoustic Space Dimensionality Selection and Combination using the Maximum Entropy Principle

Yasser H. Abdel-HaleemSteve RenalsNeil D. Lawrence
, 2004.

Abstract

In this paper we propose a discriminative approach to acoustic space dimensionality selection based on maximum entropy modelling. We form a set of constraints by composing the acoustic space with the space of phone classes, and use a continuous feature formulation of maximum entropy modelling to select an optimal feature set. The suggested approach has two steps: (1) the selection of the best acoustic space that efficiently and economically represents the acoustic data and its variability; (2) the combination of selected acoustic features in the maximum entropy framework to estimate the posterior probabilities over the phonetic labels given the acoustic input. Specific contributions of this paper include a parameter estimation algorithm (generalized improved iterative scaling) that enables the use of negative features, the parameterization of constraint functions using Gaussian mixture models, and experimental results using the TIMIT database.

Cite this Paper


BibTeX
@InProceedings{pmlr-v-abdelhaleem-acoustic04, title = {Acoustic Space Dimensionality Selection and Combination using the Maximum Entropy Principle}, author = {Yasser H. Abdel-Haleem and Steve Renals and Neil D. Lawrence}, year = {}, editor = {}, url = {http://inverseprobability.com/publications/abdelhaleem-acoustic04.html}, abstract = {In this paper we propose a discriminative approach to acoustic space dimensionality selection based on maximum entropy modelling. We form a set of constraints by composing the acoustic space with the space of phone classes, and use a continuous feature formulation of maximum entropy modelling to select an optimal feature set. The suggested approach has two steps: (1) the selection of the best acoustic space that efficiently and economically represents the acoustic data and its variability; (2) the combination of selected acoustic features in the maximum entropy framework to estimate the posterior probabilities over the phonetic labels given the acoustic input. Specific contributions of this paper include a parameter estimation algorithm (generalized improved iterative scaling) that enables the use of negative features, the parameterization of constraint functions using Gaussian mixture models, and experimental results using the TIMIT database.} }
Endnote
%0 Conference Paper %T Acoustic Space Dimensionality Selection and Combination using the Maximum Entropy Principle %A Yasser H. Abdel-Haleem %A Steve Renals %A Neil D. Lawrence %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-abdelhaleem-acoustic04 %I PMLR %J Proceedings of Machine Learning Research %P -- %U http://inverseprobability.com %V %W PMLR %X In this paper we propose a discriminative approach to acoustic space dimensionality selection based on maximum entropy modelling. We form a set of constraints by composing the acoustic space with the space of phone classes, and use a continuous feature formulation of maximum entropy modelling to select an optimal feature set. The suggested approach has two steps: (1) the selection of the best acoustic space that efficiently and economically represents the acoustic data and its variability; (2) the combination of selected acoustic features in the maximum entropy framework to estimate the posterior probabilities over the phonetic labels given the acoustic input. Specific contributions of this paper include a parameter estimation algorithm (generalized improved iterative scaling) that enables the use of negative features, the parameterization of constraint functions using Gaussian mixture models, and experimental results using the TIMIT database.
RIS
TY - CPAPER TI - Acoustic Space Dimensionality Selection and Combination using the Maximum Entropy Principle AU - Yasser H. Abdel-Haleem AU - Steve Renals AU - Neil D. Lawrence BT - PY - DA - ED - ID - pmlr-v-abdelhaleem-acoustic04 PB - PMLR SP - DP - PMLR EP - L1 - UR - http://inverseprobability.com/publications/abdelhaleem-acoustic04.html AB - In this paper we propose a discriminative approach to acoustic space dimensionality selection based on maximum entropy modelling. We form a set of constraints by composing the acoustic space with the space of phone classes, and use a continuous feature formulation of maximum entropy modelling to select an optimal feature set. The suggested approach has two steps: (1) the selection of the best acoustic space that efficiently and economically represents the acoustic data and its variability; (2) the combination of selected acoustic features in the maximum entropy framework to estimate the posterior probabilities over the phonetic labels given the acoustic input. Specific contributions of this paper include a parameter estimation algorithm (generalized improved iterative scaling) that enables the use of negative features, the parameterization of constraint functions using Gaussian mixture models, and experimental results using the TIMIT database. ER -
APA
Abdel-Haleem, Y.H., Renals, S. & Lawrence, N.D.. (). Acoustic Space Dimensionality Selection and Combination using the Maximum Entropy Principle. , in PMLR :-

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