I Acoustic Modeling Goal: Learn precise and generalizable models of the acoustic boundary associated with each distinctive feature Methods Large input vector space(many acoustic feature types) Regularized binary classifiers(SVMs) SVM outputs"smoothed" using dynamic programming SVM outputs converted to posterior probabi estimates once/5ms using histogramI. Acoustic Modeling • Goal: Learn precise and generalizable models of the acoustic boundary associated with each distinctive feature. • Methods: – Large input vector space (many acoustic feature types) – Regularized binary classifiers (SVMs) – SVM outputs “smoothed” using dynamic programming – SVM outputs converted to posterior probability estimates once/5ms using histogram