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

arXiv: https://arxiv.org/abs/2305.18146 · HTML: https://mlsanity.com/paper/2305.18146/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2023.

## Built on by (most cited first, 13 of 13)

- [MultiPA: A Multi-task Speech Pronunciation Assessment Model for Open Response Scenarios](https://mlsanity.com/paper/2308.12490/) (2023)
- [An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution](https://mlsanity.com/paper/2404.07575/) (2024)
- [Towards Efficient and Multifaceted Computer-assisted Pronunciation Training Leveraging Hierarchical Selective State Space Model and Decoupled Cross-entropy Loss](https://mlsanity.com/paper/2502.07575/) (2025)
- [Acoustic Feature Mixup for Balanced Multi-aspect Pronunciation Assessment](https://mlsanity.com/paper/2406.15723/) (2024)
- [Enhancing GOP in CTC-Based Mispronunciation Detection with Phonological Knowledge](https://mlsanity.com/paper/2506.02080/) (2025)
- [Evaluating Logit-Based GOP Scores for Mispronunciation Detection](https://mlsanity.com/paper/2506.12067/) (2025)
- [Segmentation-free Goodness of Pronunciation](https://mlsanity.com/paper/2507.16838/) (2025)
- [Fine-Tuning Large Multimodal Models for Automatic Pronunciation Assessment](https://mlsanity.com/paper/2509.15701/) (2025)
- [A Finetuned SpeechLLM for Joint Multi-Granular L2 Assessment and Natural-Language Rationales](https://mlsanity.com/paper/2606.09470/) (2026)
- [An Effective Strategy for Modeling Score Ordinality and Non-uniform Intervals in Automated Speaking Assessment](https://mlsanity.com/paper/2509.03372/) (2025)
- [Multi-task Pretraining for Enhancing Interpretable L2 Pronunciation Assessment](https://mlsanity.com/paper/2509.16876/) (2025)
- [MuFFIN: Multifaceted Pronunciation Feedback Model with Interactive Hierarchical Neural Modeling](https://mlsanity.com/paper/2510.04956/) (2025)
- [Multi-granularity Interactive Attention Framework for Residual Hierarchical Pronunciation Assessment](https://mlsanity.com/paper/2601.01745/) (2026)

## Abstract (arXiv)

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.