Reference : Experiments of ASR-based mispronunciation detection for children and adult English le...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/51660
Experiments of ASR-based mispronunciation detection for children and adult English learners
English
Hosseini Kivanani, Nina mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Gretter, Roberto mailto []
Matassoni, Marco mailto []
Falavigna, Giuseppe Daniele mailto []
Nov-2021
BNAIC/BeneLearn 2021
Hosseini Kivanani, Nina mailto
Gretter, Roberto mailto
Matassoni, Marco mailto
Falavigna, Giuseppe Daniele mailto
BnL
203-216
Yes
0-2799-2527-X
33rd Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning
10/11/2021-12/11/2021
Proceedings of BNAIC/BeneLearn 2021
Luxembourg
[en] ASR ; Detection of pronunciation errors ; l2 learners
[en] Pronunciation is one of the fundamentals of language learning, and it is considered a primary factor of spoken language when it
comes to an understanding and being understood by others. The persistent presence of high error rates in speech recognition domains resulting from mispronunciations motivates us to find alternative techniques
for handling mispronunciations. In this study, we develop a mispronunciation assessment system that checks the pronunciation of non-native
English speakers, identifies the commonly mispronounced phonemes of
Italian learners of English, and presents an evaluation of the non-native
pronunciation observed in phonetically annotated speech corpora. In this
work, to detect mispronunciations, we used a phone-based ASR implemented using Kaldi. We used two non-native English labeled corpora;
(i) a corpus of Italian adults contains 5,867 utterances from 46 speakers,
and (ii) a corpus of Italian children consists of 5,268 utterances from 78
children. Our results show that the selected error model can discriminate correct sounds from incorrect sounds in both native and non-native
speech, and therefore can be used to detect pronunciation errors in nonnative speech. The phone error rates show improvement in using the
error language model. Furthermore, the ASR system shows better accuracy after applying the error model on our selected corpora.
http://hdl.handle.net/10993/51660

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
bnaic2021_preproceedings.pdfISSN 2799-2527Publisher postprint55.43 MBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.