Speak & Improve Corpus 2025: an L2 English Speech Corpus for Language Assessment and Feedback
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916528231809024 |
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| author | Knill, Kate Nicholls, Diane Gales, Mark J. F. Qian, Mengjie Stroinski, Pawel |
| author_facet | Knill, Kate Nicholls, Diane Gales, Mark J. F. Qian, Mengjie Stroinski, Pawel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11986 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Speak & Improve Corpus 2025: an L2 English Speech Corpus for Language Assessment and Feedback Knill, Kate Nicholls, Diane Gales, Mark J. F. Qian, Mengjie Stroinski, Pawel Computation and Language 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. |
| title | Speak & Improve Corpus 2025: an L2 English Speech Corpus for Language Assessment and Feedback |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2412.11986 |