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Phonetic landmark detection for automatic language identification
David Harwath
,
Mark Hasegawa-Johnson
Electrical and Computer Engineering
Coordinated Science Lab
Speech and Hearing Science
Linguistics
Beckman Institute for Advanced Science and Technology
Siebel School of Computing and Data Science
Social & Behavioral Sciences Institute
Research output
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Chapter in Book/Report/Conference proceeding
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Conference contribution
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Keyphrases
Support Vector Machine
100%
Landmark Detection
100%
Phonetics
100%
Automatic Language Identification
100%
Telephone
66%
Shifted delta Cepstral Coefficients
66%
Acoustics
33%
Concatenated
33%
Distinctive Features
33%
Position Feature
33%
Gaussian Mixture Model
33%
Recognizer
33%
Speech Corpus
33%
Telephone Speech
33%
NTIMIT
33%
Phoneme Classification
33%
Hybrid Feature Vector
33%
Computer Science
Telephone
100%
Landmark Detection
100%
Support Vector Machine
100%
Language Identification
100%
Cepstral Coefficient
66%
Distinctive Feature
33%
Gaussian Mixture Model
33%
Feature Vector
33%
Recognizer
33%
speech corpus
33%
Physics
Gaussian Distribution
100%
Phoneme
100%