# 3M: An Effective Multi-view, Multi-granularity, and Multi-aspect Modeling Approach to English Pronunciation Assessment

arXiv: https://arxiv.org/abs/2208.09110 · HTML: https://mlsanity.com/paper/2208.09110/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2022.

## Built on by (most cited first, 22 of 22)

- [Leveraging Allophony in Self-Supervised Speech Models for Atypical Pronunciation Assessment](https://mlsanity.com/paper/2502.07029/) (2025)
- [Hierarchical Pronunciation Assessment with Multi-Aspect Attention](https://mlsanity.com/paper/2211.08102/) (2022)
- [MultiPA: A Multi-task Speech Pronunciation Assessment Model for Open Response Scenarios](https://mlsanity.com/paper/2308.12490/) (2023)
- [Automatic Pronunciation Assessment -- A Review](https://mlsanity.com/paper/2310.13974/) (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)
- [Score-balanced Loss for Multi-aspect Pronunciation Assessment](https://mlsanity.com/paper/2305.16664/) (2023)
- [Pronunciation Assessment with Multi-modal Large Language Models](https://mlsanity.com/paper/2407.09209/) (2024)
- [Phonetic and Prosody-aware Self-supervised Learning Approach for Non-native Fluency Scoring](https://mlsanity.com/paper/2305.11438/) (2023)
- [L1-aware Multilingual Mispronunciation Detection Framework](https://mlsanity.com/paper/2309.07719/) (2023)
- [Preserving Phonemic Distinctions for Ordinal Regression: A Novel Loss Function for Automatic Pronunciation Assessment](https://mlsanity.com/paper/2310.01839/) (2023)
- [Acoustic Feature Mixup for Balanced Multi-aspect Pronunciation Assessment](https://mlsanity.com/paper/2406.15723/) (2024)
- [SpeechBlender: Speech Augmentation Framework for Mispronunciation Data Generation](https://mlsanity.com/paper/2211.00923/) (2022)
- [Comparison of L2 Korean pronunciation error patterns from five L1 backgrounds by using automatic phonetic transcription](https://mlsanity.com/paper/2306.10821/) (2023)
- [The complementary roles of non-verbal cues for Robust Pronunciation Assessment](https://mlsanity.com/paper/2309.07739/) (2023)
- [Zero-Shot Text-to-Speech as Golden Speech Generator: A Systematic Framework and its Applicability in Automatic Pronunciation Assessment](https://mlsanity.com/paper/2409.07151/) (2024)
- [Semi-supervised Learning For Robust Speech Evaluation](https://mlsanity.com/paper/2409.14666/) (2024)
- [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)
- [Goodness-of-pronunciation without phoneme time alignment](https://mlsanity.com/paper/2603.25150/) (2026)
- [OpenPA: Forced-alignment-free Multi-granular Pronunciation Assessment with Mixed-transcript Adaptation](https://mlsanity.com/paper/2610.13826/) (2026)

## Abstract (arXiv)

As an indispensable ingredient of computer-assisted pronunciation training (CAPT), automatic pronunciation assessment (APA) plays a pivotal role in aiding self-directed language learners by providing multi-aspect and timely feedback. However, there are at least two potential obstacles that might hinder its performance for practical use. On one hand, most of the studies focus exclusively on leveraging segmental (phonetic)-level features such as goodness of pronunciation (GOP); this, however, may cause a discrepancy of feature granularity when performing suprasegmental (prosodic)-level pronunciation assessment. On the other hand, automatic pronunciation assessments still suffer from the lack of large-scale labeled speech data of non-native speakers, which inevitably limits the performance of pronunciation assessment. In this paper, we tackle these problems by integrating multiple prosodic and phonological features to provide a multi-view, multi-granularity, and multi-aspect (3M) pronunciation modeling. Specifically, we augment GOP with prosodic and self-supervised learning (SSL) features, and meanwhile develop a vowel/consonant positional embedding for a more phonology-aware automatic pronunciation assessment. A series of experiments conducted on the publicly-available speechocean762 dataset show that our approach can obtain significant improvements on several assessment granularities in comparison with previous work, especially on the assessment of speaking fluency and speech prosody.