English Lexical Stress Detection and Sentence-Based Intonation Assessment based on Contour Shape Description
Date Issued
2015
Date
2015
Author(s)
Tsai, Sheng-Chi
Abstract
Lexical stress and sentence intonation play important role in communication, both are related to emotion, attitude and the meaning that he/she wants to convey. This thesis is divided into two parts: the first part describes about lexical stress detection and the second part describes about sentence-based intonation assessment. In terms of lexical stress detection, energy-related features, pitch-related features, duration and contour shape features are extracted. All of these features are vowel-based. In this thesis, the performances of system using different single features are compared. Also, the performance of system using different combinations of features are compared. Based on them, it is contour shape features that are found useful in lexical stress detection. Besides, the different methods of classification by training syllable number-dependent classifiers and syllable number-independent classifiers are compared. The best recognition rate of our system is 90.83%. In terms of intonation assessment, it is treated as a classification problem, and the features being extracted include two parts: the intonation similarity of sentences and the intonation similarity of words. The features that we used are correlation coefficient, distance of dynamic time warping and contour shape-related features between two pitch contours. Besides, performance evaluation is determined by Pearson’s correlation coefficient, and it reaches 0.35 in our system.
Subjects
lexical stress detection
sentence-based intonation assessment
speech assessment
contour shape feature
computer-assisted language learning
Type
thesis
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