Talk outline Overview 1. Acoustic Modeling Speech data and acoustic features Landmark detection Estimation of real-valued"distinctive features" using support vector machines(SVM 2. Pronunciation Modeling A Dynamic Bayesian network(DBn)implementation of Articulatory Phonology A Discriminative Pronunciation model implemented using Maximum Entropy(MaxEnt) 3. Technological Evaluation Rescoring of word lattice output from an hMm-based recognizer New errors that we caused: Pronunciation models trained on 3 hours can't compete with triphone models trained on 3000 hours Future plansTalk Outline Overview 1. Acoustic Modeling – Speech data and acoustic features – Landmark detection – Estimation of real-valued “distinctive features” using support vector machines (SVM) 2. Pronunciation Modeling – A Dynamic Bayesian network (DBN) implementation of Articulatory Phonology – A Discriminative Pronunciation model implemented using Maximum Entropy (MaxEnt) 3. Technological Evaluation – Rescoring of word lattice output from an HMM-based recognizer – Errors that we fixed: Channel noise, Laughter, etcetera – New errors that we caused: Pronunciation models trained on 3 hours can’t compete with triphone models trained on 3000 hours. – Future Plans