The complementary roles of non-verbal cues for Robust Pronunciation Assessment
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Abstract (arXiv)
Research on pronunciation assessment systems focuses on utilizing phonetic and phonological aspects of non-native (L2) speech, often neglecting the rich layer of information hidden within the non-verbal cues. In this study, we proposed a novel pronunciation assessment framework, IntraVerbalPA. % The framework innovatively incorporates both fine-grained frame- and abstract utterance-level non-verbal cues, alongside the conventional speech and phoneme representations. Additionally, we introduce ''Goodness of phonemic-duration'' metric to effectively model duration distribution within the framework. Our results validate the effectiveness of the proposed IntraVerbalPA framework and its individual components, yielding performance that either matches or outperforms existing research works.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 22 references that are in the map. First posted on arXiv 14 Sep 2023.
Builds on
9 of 22 references.
References · 9
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
- 2020Unsupervised Cross-lingual Representation Learning for Speech Recognitioncited by 501
- 2022Transformer-Based Multi-Aspect Multi-Granularity Non-Native English Speaker Pronunciation Assessmentcited by 37
- 2022Automatic Pronunciation Assessment using Self-Supervised Speech Representation Learningcited by 29
- 2021Phone-to-audio alignment without text: A Semi-supervised Approachcited by 26
- 20223M: An Effective Multi-view, Multi-granularity, and Multi-aspect Modeling Approach to English Pronunciation Assessmentcited by 22
- 2021Multilingual Speech Evaluation: Case Studies on English, Malay and Tamilcited by 10
- 2022Improving Mispronunciation Detection with Wav2vec2-based Momentum Pseudo-Labeling for Accentedness and Intelligibility Assessmentcited by 10
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Citing papers per year: 2023: 1.
1 paper.
- 2023Automatic Pronunciation Assessment -- A Reviewcited by 7
- 2023Automatic Pronunciation Assessment -- A Review21 Oct 2023