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AUTOMATIC DETECTION OF SYLLABLE STRESS USING SONORITY BASED PROMINENCE FEATURES FOR PRONUNCIATION EVALUATION

Abstract: 

Automatic syllable stress detection is useful in assessing and diagnosing the quality of the pronunciation of second language (L2) learners in an automated way. Typically, the syllable stress depends on three prominence measures -- intensity level, duration, pitch -- around the sound unit with the highest sonority in the respective syllable. Stress detection is often formulated as a binary classification task using cues from the feature contours representing the prominence measures. We observe that cues from a feature contour obtained by incorporating relative sonority levels in the prominence measures are more indicative of the syllable stress compared to those from the feature contours representing only the prominence measures. Based on this observation, we propose a new feature contour based on temporal correlation selected sub-band correlation with an optimal set of sub-bands, called sonorous sub-bands, to maximize the stress detection accuracy. Experiments on ISLE corpus show that, for German and Italian non-native English speakers, the syllable stress detection accuracies (87.53% and 86.26%) are higher when the proposed features are used compared to the baseline accuracies (85.81% and 83.17%) indicating the effectiveness of the sonority based prominence features.

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Paper Details

Authors:
Chiranjeevi Yarra, Om D Deshmukh, Prasanta Kumar Ghosh
Submitted On:
11 March 2017 - 8:49pm
Short Link:
Type:
Poster
Event:
Document Year:
2017
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Document Files

ICASSP17.pdf

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[1] Chiranjeevi Yarra, Om D Deshmukh, Prasanta Kumar Ghosh, "AUTOMATIC DETECTION OF SYLLABLE STRESS USING SONORITY BASED PROMINENCE FEATURES FOR PRONUNCIATION EVALUATION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1472. Accessed: May. 19, 2019.
@article{1472-17,
url = {http://sigport.org/1472},
author = {Chiranjeevi Yarra; Om D Deshmukh; Prasanta Kumar Ghosh },
publisher = {IEEE SigPort},
title = {AUTOMATIC DETECTION OF SYLLABLE STRESS USING SONORITY BASED PROMINENCE FEATURES FOR PRONUNCIATION EVALUATION},
year = {2017} }
TY - EJOUR
T1 - AUTOMATIC DETECTION OF SYLLABLE STRESS USING SONORITY BASED PROMINENCE FEATURES FOR PRONUNCIATION EVALUATION
AU - Chiranjeevi Yarra; Om D Deshmukh; Prasanta Kumar Ghosh
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1472
ER -
Chiranjeevi Yarra, Om D Deshmukh, Prasanta Kumar Ghosh. (2017). AUTOMATIC DETECTION OF SYLLABLE STRESS USING SONORITY BASED PROMINENCE FEATURES FOR PRONUNCIATION EVALUATION. IEEE SigPort. http://sigport.org/1472
Chiranjeevi Yarra, Om D Deshmukh, Prasanta Kumar Ghosh, 2017. AUTOMATIC DETECTION OF SYLLABLE STRESS USING SONORITY BASED PROMINENCE FEATURES FOR PRONUNCIATION EVALUATION. Available at: http://sigport.org/1472.
Chiranjeevi Yarra, Om D Deshmukh, Prasanta Kumar Ghosh. (2017). "AUTOMATIC DETECTION OF SYLLABLE STRESS USING SONORITY BASED PROMINENCE FEATURES FOR PRONUNCIATION EVALUATION." Web.
1. Chiranjeevi Yarra, Om D Deshmukh, Prasanta Kumar Ghosh. AUTOMATIC DETECTION OF SYLLABLE STRESS USING SONORITY BASED PROMINENCE FEATURES FOR PRONUNCIATION EVALUATION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1472