NDLI: Supervised detection and unsupervised discovery of pronunciation error patterns for computer-assisted language learning

Please wait, while we are loading the content...

Supervised detection and unsupervised discovery of pronunciation error patterns for computer-assisted language learning

Content Provider	ACM Digital Library
Author	Wang, Yow-Bang Lee, Lin-shan
Copyright Year	2016
Abstract	Pronunciation error patterns (EPs) are patterns of mispronunciation frequently produced by language learners, and are usually different for different pairs of target and native languages. Accurate information of EPs can offer helpful feedbacks to the learners to improve their language skills. However, the major difficulty of EP detection comes from the fact that EPs are intrinsically similar to their corresponding canonical pronunciation, and different EPs corresponding to same canonical pronunciation are also intrinsically similar to each other. As a result, distinguishing EPs from their corresponding canonical pronunciation and between different EPs of the same phoneme is a difficult task-perhaps even more difficult than distinguishing between different phonemes in one language. On the other hand, the cost of deriving all EPs for each pair of target and native languages is high, usually requiring extensive expert knowledge or high-quality annotated data. Unsupervised EP discovery from a corpus of learner recordings would thus be an attractive addition to the field. In this paper, we propose new frameworks for both supervised EP detection and unsupervised EP discovery. For supervised EP detection, we use hierarchical multi-layer perceptrons (MLPs) as the EP classifiers to be integrated with the baseline using HMM/GMM in a two-pass Viterbi decoding architecture. Experimental results show that the new framework enhances the power of EP diagnosis. For unsupervised EP discovery we propose the first known framework, using the hierarchical agglomerative clustering (HAC) algorithm to explore sub-segmental variation within phoneme segments and produce fixed-length segment-level feature vectors in order to distinguish different EPs. We tested K-means (assuming a known number of EPs) and the Gaussian mixture model with the minimum description length principle (estimating an unknown number of EPs) for EP discovery. Preliminary experiments offered very encouraging results, although there is still a long way to go to approach the performance of human experts. We also propose to use the universal phoneme posteriorgram (UPP), derived from an MLP trained on corpora of mixed languages, as frame-level features in both supervised detection and unsupervised discovery of EPs. Experimental results show that using UPP not only achieves the best performance, but also is useful in analyzing the mispronunciation produced by language learners.
Starting Page	564
Ending Page	579
Page Count	16
File Format	PDF
ISSN	23299290
e-ISSN	23299304
DOI	10.1109/TASLP.2014.2387413
Volume Number	23
Issue Number	3
Journal	IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-03-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Computer-aided pronunciation training Computer-assisted language learning Error pattern detection Error pattern discovery Universal phoneme posteriorgram
Content Type	Text
Resource Type	Article
Subject	Instrumentation Computational Mathematics Signal Processing Electrical and Electronic Engineering Acoustics and Ultrasonics Speech and Hearing Media Technology

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Toward unsupervised discovery of pronunciation error patterns using universal phoneme posteriorgram for computer-assisted language learning

A recursive dialogue game for personalized computer-aided pronunciation training

A Recursive Dialogue Game for Personalized Computer-Aided Pronunciation Training

A two-pass framework of mispronunciation detection and diagnosis for computer-aided pronunciation training

Automatic pronunciation scoring with score combination by learning to rank and class-normalized DP-based quantization

Automatic Pronunciation Scoring with Score Combination by Learning to Rank and Class-Normalized DP-Based Quantization

On training targets for supervised speech separation

Maximum F1-score discriminative training criterion for automatic mispronunciation detection

On Training Targets for Supervised Speech Separation

Supervised detection and unsupervised discovery of pronunciation error patterns for computer-assisted language learning

Similar Documents

Toward unsupervised discovery of pronunciation error patterns using universal phoneme posteriorgram for computer-assisted language learning

A recursive dialogue game for personalized computer-aided pronunciation training

A Recursive Dialogue Game for Personalized Computer-Aided Pronunciation Training

A two-pass framework of mispronunciation detection and diagnosis for computer-aided pronunciation training

Automatic pronunciation scoring with score combination by learning to rank and class-normalized DP-based quantization

Automatic Pronunciation Scoring with Score Combination by Learning to Rank and Class-Normalized DP-Based Quantization

On training targets for supervised speech separation

Maximum F1-score discriminative training criterion for automatic mispronunciation detection

On Training Targets for Supervised Speech Separation

Supervised detection and unsupervised discovery of pronunciation error patterns for computer-assisted language learning