Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention - Publication - Bridge of Knowledge

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Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention

Abstract

This paper describes two novel complementary techniques that improve the detection of lexical stress errors in non-native (L2) English speech: attention-based feature extraction and data augmentation based on Neural Text-To-Speech (TTS). In a classical approach, audio features are usually extracted from fixed regions of speech such as the syllable nucleus. We propose an attention-based deep learning model that automatically de rives optimal syllable-level representation from frame-level and phoneme-level audio features. Training this model is challenging because of the limited amount of incorrect stress patterns. To solve this problem, we propose to augment the training set with incorrectly stressed words generated with Neural TTS. Combining both techniques achieves 94.8% precision and 49.2% recall for the detection of incorrectly stressed words in L2 English speech of Slavic and Baltic speakers.

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DOI:
Digital Object Identifier (open in new tab) 10.21437/Interspeech.2021-86
License
Copyright (2021 ISCA)

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Details

Category:
Conference activity
Type:
publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
Language:
English
Publication year:
2021
Bibliographic description:
Korzekwa D., Barra-Chicote R., Zaporowski S., Beringer G., Lorenzo-Trueba J., Serafinowicz A., Droppo J., Drugman T., Kostek B.: Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention// / : , 2021,
DOI:
Digital Object Identifier (open in new tab) 10.21437/interspeech.2021-86
Verified by:
Gdańsk University of Technology

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