Overview History Research described in this talk was performed between June 30 and August 17, 2004, at the Johns Hopkins summer workshop WS04 Scientific goal To use high-dimensional machine learning technologies (SVM, DBn to create representations capable of learning from data, the types of speech knowledge that humans exhibit in psychophysical speech perception experiments Technological Goal Long-term: To create a better speech recognizer Short-term: lattice rescoring, applied to word lattices produced by SrIs nn/hmm hybridOverview • History – Research described in this talk was performed between June 30 and August 17, 2004, at the Johns Hopkins summer workshop WS04 • Scientific Goal – To use high-dimensional machine learning technologies (SVM, DBN) to create representations capable of learning, from data, the types of speech knowledge that humans exhibit in psychophysical speech perception experiments • Technological Goal – Long-term: To create a better speech recognizer – Short-term: lattice rescoring, applied to word lattices produced by SRI’s NN/HMM hybrid