Hierarchical Pronunciation Assessment with Multi-Aspect Attention

Heejin Do; Yunsu Kim; Gary Geunbae Lee

doi:10.1109/icassp49357.2023.10095733

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arXiv · 2211.08102

Hierarchical Pronunciation Assessment with Multi-Aspect Attention

Heejin Do·Yunsu Kim·Gary Geunbae Lee

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Abstract

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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Heejin Do, Yunsu Kim, Gary Geunbae Lee. 2022-11-15. Hierarchical Pronunciation Assessment with Multi-Aspect Attention. https://doi.org/10.1109/icassp49357.2023.10095733

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