A generative student model for scoring word reading skills

IEEE Transactions on Audio, Speech and Language Processing

Volumn 19, Issue 2, 2011, Pages 348-360

  Tepperman, Joseph ^a   Lee, Sungbok ^b   Narayanan, Shrikanth ^b   Alwan, Abeer ^c  

^a Rosetta Stone Labs   (United States)

^b University of Southern California   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Bayesian networks; children's speech; pronunciation evaluation; reading assessment; student modeling

Indexed keywords

AUTOMATIC METHOD; BAYES NET; CHILDREN'S SPEECH; CLASSROOM SETTINGS; COGNITIVE STATE; DATA SETS; DATA-DRIVEN; EXPERT KNOWLEDGE; FOREIGN LANGUAGE; HUMAN ASSESSMENT; HUMAN LISTENERS; NEW MODEL; NON-NATIVE SPEAKERS; PRIOR KNOWLEDGE; PRONUNCIATION EVALUATION; PRONUNCIATION VARIANTS; READING ASSESSMENT; STUDENT MODELING; STUDENT MODELS; STUDENT POPULATIONS;

BAYESIAN NETWORKS; DISTRIBUTED PARAMETER NETWORKS; INFERENCE ENGINES; INTELLIGENT NETWORKS; KNOWLEDGE BASED SYSTEMS; SPEECH RECOGNITION; STATISTICAL TESTS; TEACHING;

STUDENTS;

EID: 78049271417     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASL.2010.2047812     Document Type: Article

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