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A Hierarchical Context-aware Modeling Approach for Multi-aspect and Multi-granular Pronunciation Assessment
Fu-An Chao, Tien-Hong Lo, Tzu-I Wu, Yao-Ting Sung, Berlin Chen
Automatic Pronunciation Assessment (APA) plays a vital role in Computer-assisted Pronunciation Training (CAPT) when evaluating a second language (L2) learner's speaking proficiency. However, an apparent downside of most de facto methods is that they parallelize the modeling process throughout different speech granularities without accounting for the hierarchical and local contextual relationships among them. In light of this, a novel hierarchical approach is proposed in this paper for multi-aspect and multi-granular APA. Specifically, we first introduce the notion of sup-phonemes to explore more subtle semantic traits of L2 speakers. Second, a depth-wise separable convolution layer is exploited to better encapsulate the local context cues at the sub-word level. Finally, we use a score-restraint attention pooling mechanism to predict the sentence-level scores and optimize the component models with a multitask learning (MTL) framework. Extensive experiments carried out on a publicly-available benchmark dataset, viz. speechocean762, demonstrate the efficacy of our approach in relation to some cutting-edge baselines.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Phone-level pronunciation scoring | speechocean762 | 3MH | Pearson correlation coefficient (PCC) | 0.693 | #2 of 8 | Archive leaderboard | report |
| Utterance-level pronounciation scoring | speechocean762 | 3MH | Pearson correlation coefficient (PCC) | 0.811 | #1 of 5 | Archive leaderboard | report |
| Word-level pronunciation scoring | speechocean762 | 3MH | Pearson correlation coefficient (PCC) | 0.694 | #1 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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