# Speak & Improve Challenge 2025: Tasks and Baseline Systems

arXiv: https://arxiv.org/abs/2412.11985 · HTML: https://mlsanity.com/paper/2412.11985/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 35 references that are in the map.
- First posted on arXiv 16 Dec 2024.

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

- [Assessment of L2 Oral Proficiency using Speech Large Language Models](https://mlsanity.com/paper/2505.21148/) (2025)
- [Natural Language-based Assessment of L2 Oral Proficiency using LLMs](https://mlsanity.com/paper/2507.10200/) (2025)
- [Opportunities and Challenges of LLMs in Education: An NLP Perspective](https://mlsanity.com/paper/2507.22753/) (2025)
- [The NTNU System at the S&I Challenge 2025 SLA Open Track](https://mlsanity.com/paper/2506.05121/) (2025)
- [Scaling and Prompting for Improved End-to-End Spoken Grammatical Error Correction](https://mlsanity.com/paper/2505.21137/) (2025)
- [Acoustically Precise Hesitation Tagging Is Essential for End-to-End Verbatim Transcription Systems](https://mlsanity.com/paper/2506.04076/) (2025)
- [Advancing Automated Speaking Assessment Leveraging Multifaceted Relevance and Grammar Information](https://mlsanity.com/paper/2506.16285/) (2025)
- [One Whisper to Grade Them All](https://mlsanity.com/paper/2507.17918/) (2025)
- [End-to-End Spoken Grammatical Error Correction](https://mlsanity.com/paper/2506.18532/) (2025)
- [Data Augmentation for Spoken Grammatical Error Correction](https://mlsanity.com/paper/2507.19374/) (2025)
- [Listen, Correct, and Feed Back: Spoken Pedagogical Feedback Generation](https://mlsanity.com/paper/2604.14177/) (2026)
- [Bias Analysis of L2 Speaking Assessment Systems Using Concept Activation Vectors](https://mlsanity.com/paper/2608.06300/) (2026)
- [CASA: Content-Acoustic Speaking Assessment with Speech Encoder and Large Language Model](https://mlsanity.com/paper/2608.13101/) (2026)

## Abstract (arXiv)

This paper presents the "Speak & Improve Challenge 2025: Spoken Language Assessment and Feedback" -- a challenge associated with the ISCA SLaTE 2025 Workshop. The goal of the challenge is to advance research on spoken language assessment and feedback, with tasks associated with both the underlying technology and language learning feedback. Linked with the challenge, the Speak & Improve (S&I) Corpus 2025 is being pre-released, a dataset of L2 learner English data with holistic scores and language error annotation, collected from open (spontaneous) speaking tests on the Speak & Improve learning platform. The corpus consists of approximately 315 hours of audio data from second language English learners with holistic scores, and a 55-hour subset with manual transcriptions and error labels. The Challenge has four shared tasks: Automatic Speech Recognition (ASR), Spoken Language Assessment (SLA), Spoken Grammatical Error Correction (SGEC), and Spoken Grammatical Error Correction Feedback (SGECF). Each of these tasks has a closed track where a predetermined set of models and data sources are allowed to be used, and an open track where any public resource may be used. Challenge participants may do one or more of the tasks. This paper describes the challenge, the S&I Corpus 2025, and the baseline systems released for the Challenge.