Papers › Hierarchical Pronunciation Assessment with Multi-Aspect Attention
Hierarchical Pronunciation Assessment with Multi-Aspect Attention
Heejin Do, Yunsu Kim, Gary Geunbae Lee
Automatic pronunciation assessment is a major component of a computer-assisted pronunciation training system. To provide in-depth feedback, scoring pronunciation at various levels of granularity such as phoneme, word, and utterance, with diverse aspects such as accuracy, fluency, and completeness, is essential. However, existing multi-aspect multi-granularity methods simultaneously predict all aspects at all granularity levels; therefore, they have difficulty in capturing the linguistic hierarchy of phoneme, word, and utterance. This limitation further leads to neglecting intimate cross-aspect relations at the same linguistic unit. In this paper, we propose a Hierarchical Pronunciation Assessment with Multi-aspect Attention (HiPAMA) model, which hierarchically represents the granularity levels to directly capture their linguistic structures and introduces multi-aspect attention that reflects associations across aspects at the same level to create more connotative representations. By obtaining relational information from both the granularity- and aspect-side, HiPAMA can take full advantage of multi-task learning. Remarkable improvements in the experimental results on the speachocean762 datasets demonstrate the robustness of HiPAMA, particularly in the difficult-to-assess aspects.
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Code
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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 | HiPAMA-Librispeech | Pearson correlation coefficient (PCC) | 0.62 | #6 of 8 | Archive leaderboard | report |
| Utterance-level pronounciation scoring | speechocean762 | HiPAMA-Librispeech | Pearson correlation coefficient (PCC) | 0.754 | #3 of 5 | Archive leaderboard | report |
| Word-level pronunciation scoring | speechocean762 | HiPAMA-Librispeech | Pearson correlation coefficient (PCC) | 0.59 | #4 of 5 | Archive leaderboard | report |
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