Westman, Champlin & Kelly
Parameter learning in a hidden trajectory model
Parameters for distributions of a hidden trajectory model including means and variances are estimated using an acoustic likelihood function for observation vectors as an objection function for optimization. The estimation includes only acoustic data and not any intermediate estimate on hidden dynamic variables. Gradient ascent methods can be developed for optimizing the acoustic likelihood function.
Microsoft Corporation
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G10L G10L
additional inventional
Alejandro Dong Xiaolong Li
Acero Yu Li Deng