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Dynamic evidence models in a DBN phone recognizer
William Schuler
, Tim Miller
, Stephen Wu
,
Andrew Exley
Computer Science and Engineering
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
1
Scopus citations
Overview
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Dive into the research topics of 'Dynamic evidence models in a DBN phone recognizer'. Together they form a unique fingerprint.
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Keyphrases
Evidence Model
100%
Phone Recognizer
100%
Dynamic Bayes Network
100%
Conditional Random Fields
75%
Random Variables
25%
Gaussian Mixture Model
25%
Hidden Markov Model
25%
Recognition Task
25%
Conditionally Independent
25%
Transitional Dynamics
25%
Field Dynamics
25%
Variable States
25%
Conditional Probability Distribution
25%
First-order Features
25%
Hidden Variables
25%
Hypothesis Space
25%
Linguistic Phenomena
25%
TIMIT
25%
MFCC Features
25%
Acoustical Model
25%
Formant Targets
25%
Random Variable Distribution
25%
Net Topology
25%
Phone Recognition
25%
Formant Trajectory
25%
Mathematics
Conditionals
100%
Hidden Markov Models
100%
Random Field
100%
Random Variable
66%
Probability Distribution
33%
Conditional Probability
33%
Hidden Variable
33%
Gaussian Mixture Model
33%
Observed Evidence
33%
Hypothesis Space
33%
Computer Science
Conditional Random Field
100%
Recognizer
100%
Evidence Dynamic
100%
Random Variable
66%
Conditional Probability
33%
Gaussian Mixture Model
33%
Hypothesis Space
33%
Hidden Variable
33%
Mel-Frequency Cepstral Coefficients
33%
Engineering
Random Field
100%
Recognizer
100%
Random Variable ξ
66%
Gaussian Mixture Model
33%
State Variable
33%
Conditional Probability
33%
Economics, Econometrics and Finance
Hidden Markov Models
100%