# Speak & Improve Corpus 2025: an L2 English Speech Corpus for Language Assessment and Feedback

arXiv: https://arxiv.org/abs/2412.11986 · HTML: https://mlsanity.com/paper/2412.11986/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs](https://mlsanity.com/paper/2402.03927/) (2024)
- [speechocean762: An Open-Source Non-native English Speech Corpus For Pronunciation Assessment](https://mlsanity.com/paper/2104.01378/) (2021)
- [TLT-school: a Corpus of Non Native Children Speech](https://mlsanity.com/paper/2001.08051/) (2020)
- [LearnerVoice: A Dataset of Non-Native English Learners' Spontaneous Speech](https://mlsanity.com/paper/2407.04280/) (2024)

## Built on by (most cited first, 14 of 14)

- [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)
- [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)
- [Beyond Modality Limitations: A Unified MLLM Approach to Automated Speaking Assessment with Effective Curriculum Learning](https://mlsanity.com/paper/2508.12591/) (2025)
- [Proficiency-Aware Adaptation and Data Augmentation for Robust L2 ASR](https://mlsanity.com/paper/2510.10738/) (2025)
- [Controlling Implicit Shortcut Reliance in L2 Spoken English Auto-markers](https://mlsanity.com/paper/2607.16085/) (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)

We introduce the Speak & Improve Corpus 2025, 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 aim of the corpus release is to address a major challenge to developing L2 spoken language processing systems, the lack of publicly available data with high-quality annotations. It is being made available for non-commercial use on the ELiT website. In designing this corpus we have sought to make it cover a wide-range of speaker attributes, from their L1 to their speaking ability, as well as providing manual annotations. This enables a range of language-learning tasks to be examined, such as assessing speaking proficiency or providing feedback on grammatical errors in a learner's speech. Additionally the data supports research into the underlying technology required for these tasks including automatic speech recognition (ASR) of low resource L2 learner English, disfluency detection or spoken grammatical error correction (GEC). The corpus consists of around 315 hours of L2 English learners audio with holistic scores, and a subset of audio annotated with transcriptions and error labels.