# ASR-Free Pronunciation Assessment

arXiv: https://arxiv.org/abs/2005.11902 · HTML: https://mlsanity.com/paper/2005.11902/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2020.

## Built on by (most cited first, 12 of 12)

- [Proficiency assessment of L2 spoken English using wav2vec 2.0](https://mlsanity.com/paper/2210.13168/) (2022)
- [Multilingual Speech Evaluation: Case Studies on English, Malay and Tamil](https://mlsanity.com/paper/2107.03675/) (2021)
- [Speech Intelligibility Assessment of Dysarthric Speech by using Goodness of Pronunciation with Uncertainty Quantification](https://mlsanity.com/paper/2305.18392/) (2023)
- [Mispronunciation Detection in Non-native (L2) English with Uncertainty Modeling](https://mlsanity.com/paper/2101.06396/) (2021)
- [Weakly-supervised word-level pronunciation error detection in non-native English speech](https://mlsanity.com/paper/2106.03494/) (2021)
- [Computer-assisted Pronunciation Training -- Speech synthesis is almost all you need](https://mlsanity.com/paper/2207.00774/) (2022)
- [L2 proficiency assessment using self-supervised speech representations](https://mlsanity.com/paper/2211.08849/) (2022)
- [Addressing Cold Start Problem for End-to-end Automatic Speech Scoring](https://mlsanity.com/paper/2306.14310/) (2023)
- [Improving End-To-End Modeling for Mispronunciation Detection with Effective Augmentation Mechanisms](https://mlsanity.com/paper/2110.08731/) (2021)
- [Automated evaluation of children's speech fluency for low-resource languages](https://mlsanity.com/paper/2505.19671/) (2025)
- [Exploring Non-Autoregressive End-To-End Neural Modeling For English Mispronunciation Detection And Diagnosis](https://mlsanity.com/paper/2111.00844/) (2021)
- [Multi-granularity Interactive Attention Framework for Residual Hierarchical Pronunciation Assessment](https://mlsanity.com/paper/2601.01745/) (2026)

## Abstract (arXiv)

Most of the pronunciation assessment methods are based on local features derived from automatic speech recognition (ASR), e.g., the Goodness of Pronunciation (GOP) score. In this paper, we investigate an ASR-free scoring approach that is derived from the marginal distribution of raw speech signals. The hypothesis is that even if we have no knowledge of the language (so cannot recognize the phones/words), we can still tell how good a pronunciation is, by comparatively listening to some speech data from the target language. Our analysis shows that this new scoring approach provides an interesting correction for the phone-competition problem of GOP. Experimental results on the ERJ dataset demonstrated that combining the ASR-free score and GOP can achieve better performance than the GOP baseline.