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

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arxiv 2412.11986 v2 pith:TPQQJK4D submitted 2024-12-16 cs.CL

Kate Knill , Diane Nicholls , Mark J.F. Gales , Mengjie Qian , Pawel Stroinski This is my paper · ORCID
classification cs.CL
keywords corpusenglishdataerrorimprovelanguagelearnerspeak
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Natural Language-based Assessment of L2 Oral Proficiency using LLMs

    eess.AS 2025-07 conditional novelty 6.0 of 10

    Zero-shot LLM grading of L2 speech transcripts with CEFR descriptors beats a fine-tuned BERT baseline and matches a read-aloud-trained speech model on the S&I Corpus.

  2. End-to-End Spoken Grammatical Error Correction

    cs.CL 2025-06 conditional novelty 6.0 of 10

    End-to-end Whisper models, trained with 2,500 hours of pseudo-labeled speech, fluent prompts, aligned references, and confidence filtering, outperform cascaded systems on spoken grammatical error correction and feedback.

  3. Scaling and Prompting for Improved End-to-End Spoken Grammatical Error Correction

    cs.CL 2025-05 conditional novelty 6.0 of 10

    Pseudo-labelling and prompting with fluent transcriptions improve end-to-end spoken grammatical error correction and feedback for Whisper-based models, but the benefits depend on model size.

  4. Acoustically Precise Hesitation Tagging Is Essential for End-to-End Verbatim Transcription Systems

    cs.CL 2025-06 conditional novelty 5.0 of 10

    Whisper trained with realistic 'um'/'uh' labels generated by Gemini reached 5.5% WER on L2 English speech, an 11.3% relative improvement over training with hesitations removed.

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