Title :
Automatic Syllable Stress Detection Using Prosodic Features for Pronunciation Evaluation of Language Learners
Author :
Tepperman, Joseph ; Narayanan, Shrikanth
Author_Institution :
Dept. of Electr. Eng. Syst., Univ. of Southern California, Los Angeles, CA, USA
fDate :
March 18-23, 2005
Keywords :
computational linguistics; educational aids; feature extraction; natural languages; speech recognition; vocabulary; RMS energy range; automatic syllable stress detection; expected lexical stress pattern dictionary; feature extraction; fundamental frequency slope; language learner pronunciation evaluation; language learning system; machine tutor; pronunciation errors; prosodic features; student foreign language practice; system vocabulary; Computer vision; Design engineering; Dictionaries; Humans; Laboratories; Natural languages; Speech analysis; Stress; Viterbi algorithm; Vocabulary;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on
Print_ISBN :
0-7803-8874-7
DOI :
10.1109/ICASSP.2005.1415269
