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Proceedings Paper

PCA method for automated detection of mispronounced words
Author(s): Zhenhao Ge; Sudhendu R. Sharma; Mark J. T. Smith
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Paper Abstract

This paper presents a method for detecting mispronunciations with the aim of improving Computer Assisted Language Learning (CALL) tools used by foreign language learners. The algorithm is based on Principle Component Analysis (PCA). It is hierarchical with each successive step refining the estimate to classify the test word as being either mispronounced or correct. Preprocessing before detection, like normalization and time-scale modification, is implemented to guarantee uniformity of the feature vectors input to the detection system. The performance using various features including spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs) are compared and evaluated. Best results were obtained using MFCCs, achieving up to 99% accuracy in word verification and 93% in native/non-native classification. Compared with Hidden Markov Models (HMMs) which are used pervasively in recognition application, this particular approach is computational efficient and effective when training data is limited.

Paper Details

Date Published: 3 June 2011
PDF: 11 pages
Proc. SPIE 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX, 80581D (3 June 2011); doi: 10.1117/12.884155
Show Author Affiliations
Zhenhao Ge, Purdue Univ. (United States)
Sudhendu R. Sharma, Purdue Univ. (United States)
Mark J. T. Smith, Purdue Univ. (United States)

Published in SPIE Proceedings Vol. 8058:
Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX
Harold Szu, Editor(s)

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