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Acoustic space dimensionality selection and combination using the maximum entropy principle

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publication2004 IEEE 6th Workshop on Multimedia Signal Processing
Subtitle of host publicationMMSP 2004
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages637- 640
ISBN (Print)0-7803-8578-0
Publication statusPublished - 2004
EventIEEE 6th Workshop on Multimedia Signal Processing (MMSP 2004) - Siena, Italy
Duration: 29 Sep 20041 Oct 2004


WorkshopIEEE 6th Workshop on Multimedia Signal Processing (MMSP 2004)


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 the 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.


IEEE 6th Workshop on Multimedia Signal Processing (MMSP 2004)


Siena, Italy

Event: Workshop

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