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Landmark-Based Speech Recognition: Spectrogram Reading, Support Vector Machines, Dynamic Bayesian Networks, and Phonology Mark Hasegawa-Johnson jhasegaw@uiuc.edu.

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Presentation on theme: "Landmark-Based Speech Recognition: Spectrogram Reading, Support Vector Machines, Dynamic Bayesian Networks, and Phonology Mark Hasegawa-Johnson jhasegaw@uiuc.edu."— Presentation transcript:

1 Landmark-Based Speech Recognition: Spectrogram Reading, Support Vector Machines, Dynamic Bayesian Networks, and Phonology Mark Hasegawa-Johnson University of Illinois at Urbana-Champaign, USA

2 Lecture 12. Summary, Review, and Possible Collaborations
Course Review A complete model of prosody-dependent landmark-based speech recognition Probabilistic representations of phonological concepts Two possible implementations: generative and discriminative Results so far Relationship to standard ASR methods Unsolved problems Acoustic features for speech recognition SRM training of graphical models Phonology, prosody, syntax, and semantics Graphical models of dialog structure

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