| Paper: | MLSP-P3.5 | ||
| Session: | Speech and Audio Processing | ||
| Time: | Wednesday, May 19, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Speech and Audio Processing Applications | ||
| Title: | ACOUSTIC SPACE DIMENSIONALITY SELECTION AND COMBINATION USING THE MAXIMUM ENTROPY PRINCIPLE | ||
| Authors: | Yasser Abdel-Haleem; Sheffield University | ||
| Steve Renals; University of Edinburgh | |||
| Neil Lawrence; University of Sheffield | |||
| 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 represent 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. | ||
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