NDLI: Maximum F1-score discriminative training criterion for automatic mispronunciation detection

Content Provider	ACM Digital Library
Author	Xu, Haihua Silamu, Wushour Huang, Hao Wang, Xianhui
Copyright Year	2016
Abstract	We carry out an in-depth investigation on a newly proposed Maximum F1-score Criterion (MFC) discriminative training objective function for Goodness of Pronunciation (GOP) based automatic mispronunciation detection that makes use of Gaussian Mixture Model-hidden Markov model (GMM-HMM) as acoustic models. The formulation of MFC seeks to directly optimize F1-score by converting the non-differentiable F1-score function into a continuous objective function to facilitate optimization. We present model-space training algorithm according to MFC using extended Baum-Welch form like update equations based on the weak-sense auxiliary function method. We then present MFC based feature-space discriminative training. We train a matrix projecting from posteriors of Gaussians to a normal size feature space, and add the projected features to traditional spectral features. Mispronunciation detection experiments show MFC based model-space training and feature-space training are effective in improving F1-score and other commonly used evaluation metrics. It is also shown MFC training in both the feature-space and model-space outperforms either model-space training or feature-space training alone, and is about 11.6% better than the maximum likelihood (ML) trained baseline in terms of F1-score. Further, we review and compare mispronunciation detection results with the use of MFC and some traditional training criteria that minimize word error rate in speech recognition. The experimental analysis and comparison provide useful insight into the correlations between F1-score maximization and optimization of these training criteria.
Starting Page	787
Ending Page	797
Page Count	11
File Format	PDF
ISSN	23299290
e-ISSN	23299304
DOI	10.1109/TASLP.2015.2409733
Volume Number	23
Issue Number	4
Journal	IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-04-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	F1-score Automatic mispronunciation detection Computer-assisted language learning Discriminative training Feature extraction
Content Type	Text
Resource Type	Article
Subject	Instrumentation Computational Mathematics Signal Processing Electrical and Electronic Engineering Acoustics and Ultrasonics Speech and Hearing Media Technology

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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