A Hierarchical Context-aware Modeling Approach for Multi-aspect and Multigranular Pronunciation Assessment

Fu An Chao, Tien Hong Lo, Tzu I. Wu, Yao Ting Sung, Berlin Chen

研究成果: 雜誌貢獻會議論文同行評審

摘要

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.

原文英語
頁(從 - 到)974-978
頁數5
期刊Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
2023-August
DOIs
出版狀態已發佈 - 2023
事件24th International Speech Communication Association, Interspeech 2023 - Dublin, 愛爾蘭
持續時間: 2023 8月 202023 8月 24

ASJC Scopus subject areas

  • 語言與語言學
  • 人機介面
  • 訊號處理
  • 軟體
  • 建模與模擬

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