Hierarchical Pronunciation Assessment with Multi-Aspect Attention

• URL: http://arxiv.org/abs/2211.08102v2
• Date: Fri, 26 May 2023 06:06:53 GMT
• Title: Hierarchical Pronunciation Assessment with Multi-Aspect Attention
• Authors: Heejin Do, Yunsu Kim, Gary Geunbae Lee
• Abstract summary: We propose a Hierarchical Pronunciation Assessment with Multi-aspect Attention (HiPAMA) model. HiPAMA hierarchically represents the granularity levels to directly capture their linguistic structures and introduces multi-aspect attention. Remarkable improvements in the experimental results on the speachocean datasets demonstrate the robustness of HiPAMA.
• Score: 3.6825890616838066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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