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Preferences and goals are encoded through a "loss" (or "utility") function, which measures the cost incurred by making any admissible decision for any given (hidden) state of affair.
Preferences and goals are encoded through a "loss" (or "utility") function, which measures the cost incurred by making any admissible decision for any given (hidden or unknown) state of the world.
This article proposes a new acoustic model using decision trees (DTs) as replacements for Gaussian mixture models (GMM) to compute the observation likelihoods for a given hidden Markov model state in a speech recognition system.
The DBN-TE model ensures that there is always one dominant connection between a given hidden state and its corresponding observation, while there may be many weak connections to past and future hidden states.
Each hidden state is defined with its emission probabilities (the probability to observe a marker with heterozygosity h within a given hidden state).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com