# Towards few-shot isolated word reading assessment

arXiv: https://arxiv.org/abs/2507.12217 · HTML: https://mlsanity.com/paper/2507.12217/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 16 Jul 2025.

## Builds on (most cited first, 12 of 12)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [mHuBERT-147: A Compact Multilingual HuBERT Model](https://mlsanity.com/paper/2406.06371/) (2024)
- [A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion](https://mlsanity.com/paper/2111.02392/) (2021)
- [Are discrete units necessary for Spoken Language Modeling?](https://mlsanity.com/paper/2203.05936/) (2022)
- [Acoustic word embeddings for zero-resource languages using self-supervised contrastive learning and multilingual adaptation](https://mlsanity.com/paper/2103.10731/) (2021)
- [Target Speaker ASR with Whisper](https://mlsanity.com/paper/2409.09543/) (2024)
- [Spoken-Term Discovery using Discrete Speech Units](https://mlsanity.com/paper/2408.14390/) (2024)
- [Adapting Whisper for Regional Dialects: Enhancing Public Services for Vulnerable Populations in the United Kingdom](https://mlsanity.com/paper/2501.08502/) (2025)
- [Speech Recognition for Automatically Assessing Afrikaans and isiXhosa Preschool Oral Narratives](https://mlsanity.com/paper/2501.06478/) (2025)

## Built on by (most cited first, 1 of 1)

- [Light-weight Pronunciation Assessment via Discrete Speech Token Surprisal](https://mlsanity.com/paper/2606.19910/) (2026)

## Abstract (arXiv)

We explore an ASR-free method for isolated word reading assessment in low-resource settings. Our few-shot approach compares input child speech to a small set of adult-provided reference templates. Inputs and templates are encoded using intermediate layers from large self-supervised learned (SSL) models. Using an Afrikaans child speech benchmark, we investigate design options such as discretising SSL features and barycentre averaging of the templates. Idealised experiments show reasonable performance for adults, but a substantial drop for child speech input, even with child templates. Despite the success of employing SSL representations in low-resource speech tasks, our work highlights the limitations of SSL representations for processing child data when used in a few-shot classification system.