The nature of statistical learning theory
The nature of statistical learning theory
Dimensionality reduction of electropalatographic data using latent variable models
Speech Communication
Broadcast News Transcription Using HTK
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
A tutorial on support vector regression
Statistics and Computing
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Electropalatography is a well established technique for recording information on the patterns of contact between the tongue and the hard palate during speech, leading to a stream of binary vectors representing contacts or non-contacts between the tongue and certain positions on the hard palate. A data-driven approach to mapping the speech signal onto electropalatographic information is presented. Principal component analysis is used to model the spatial structure of the electropalatographic data and support vector regression is used to map acoustic parameters onto projections of the electropalatographic data on the principal components.