# Score-balanced Loss for Multi-aspect Pronunciation Assessment

arXiv: https://arxiv.org/abs/2305.16664 · HTML: https://mlsanity.com/paper/2305.16664/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 26 May 2023.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Delving into Deep Imbalanced Regression](https://mlsanity.com/paper/2102.09554/) (2021)
- [Balanced MSE for Imbalanced Visual Regression](https://mlsanity.com/paper/2203.16427/) (2022)
- [speechocean762: An Open-Source Non-native English Speech Corpus For Pronunciation Assessment](https://mlsanity.com/paper/2104.01378/) (2021)
- [Transformer-Based Multi-Aspect Multi-Granularity Non-Native English Speaker Pronunciation Assessment](https://mlsanity.com/paper/2205.03432/) (2022)
- [Context-aware Goodness of Pronunciation for Computer-Assisted Pronunciation Training](https://mlsanity.com/paper/2008.08647/) (2020)
- [3M: An Effective Multi-view, Multi-granularity, and Multi-aspect Modeling Approach to English Pronunciation Assessment](https://mlsanity.com/paper/2208.09110/) (2022)
- [Hierarchical Pronunciation Assessment with Multi-Aspect Attention](https://mlsanity.com/paper/2211.08102/) (2022)
- [A transfer learning based approach for pronunciation scoring](https://mlsanity.com/paper/2111.00976/) (2021)

## Built on by (most cited first, 4 of 4)

- [MultiPA: A Multi-task Speech Pronunciation Assessment Model for Open Response Scenarios](https://mlsanity.com/paper/2308.12490/) (2023)
- [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)
- [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)

## Abstract (arXiv)

With rapid technological growth, automatic pronunciation assessment has transitioned toward systems that evaluate pronunciation in various aspects, such as fluency and stress. However, despite the highly imbalanced score labels within each aspect, existing studies have rarely tackled the data imbalance problem. In this paper, we suggest a novel loss function, score-balanced loss, to address the problem caused by uneven data, such as bias toward the majority scores. As a re-weighting approach, we assign higher costs when the predicted score is of the minority class, thus, guiding the model to gain positive feedback for sparse score prediction. Specifically, we design two weighting factors by leveraging the concept of an effective number of samples and using the ranks of scores. We evaluate our method on the speechocean762 dataset, which has noticeably imbalanced scores for several aspects. Improved results particularly on such uneven aspects prove the effectiveness of our method.