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

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

URL: http://arxiv.org/abs/2412.11986v2
Date: Tue, 17 Dec 2024 16:40:27 GMT
Title: Speak & Improve Corpus 2025: an L2 English Speech Corpus for Language Assessment and Feedback
Authors: Kate Knill, Diane Nicholls, Mark J. F. Gales, Mengjie Qian, Pawel Stroinski,
Abstract summary: Speak & Improve Corpus 2025 is a dataset of L2 learner English data with holistic scores and language error annotation. The aim of the corpus release is to address a major challenge to developing L2 spoken language processing systems. It is being made available for non-commercial use on the ELiT website.
Score: 28.53752312060031
License:
Abstract: 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.

Related papers

Speak & Improve Challenge 2025: Tasks and Baseline Systems [28.877872578497854]
"Speak & Improve Challenge 2025: Spoken Language Assessment and Feedback" is 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. 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)
arXiv Detail & Related papers (2024-12-16T17:05:18Z)
Teacher Perception of Automatically Extracted Grammar Concepts for L2 Language Learning [66.79173000135717]
We apply this work to teaching two Indian languages, Kannada and Marathi, which do not have well-developed resources for second language learning. We extract descriptions from a natural text corpus that answer questions about morphosyntax (learning of word order, agreement, case marking, or word formation) and semantics (learning of vocabulary). We enlist the help of language educators from schools in North America to perform a manual evaluation, who find the materials have potential to be used for their lesson preparation and learner evaluation.
arXiv Detail & Related papers (2023-10-27T18:17:29Z)
Lip Reading for Low-resource Languages by Learning and Combining General Speech Knowledge and Language-specific Knowledge [57.38948190611797]
This paper proposes a novel lip reading framework, especially for low-resource languages. Since low-resource languages do not have enough video-text paired data to train the model, it is regarded as challenging to develop lip reading models for low-resource languages.
arXiv Detail & Related papers (2023-08-18T05:19:03Z)
Textless Unit-to-Unit training for Many-to-Many Multilingual Speech-to-Speech Translation [65.13824257448564]
This paper proposes a textless training method for many-to-many multilingual speech-to-speech translation. By treating the speech units as pseudo-text, we can focus on the linguistic content of the speech. We demonstrate that the proposed UTUT model can be effectively utilized not only for Speech-to-Speech Translation (S2ST) but also for multilingual Text-to-Speech Synthesis (T2S) and Text-to-Speech Translation (T2ST)
arXiv Detail & Related papers (2023-08-03T15:47:04Z)
Incorporating L2 Phonemes Using Articulatory Features for Robust Speech Recognition [2.8360662552057323]
This study is on the efficient incorporation of the L2 phonemes, which in this work refer to Korean phonemes, through articulatory feature analysis. We employ the lattice-free maximum mutual information (LF-MMI) objective in an end-to-end manner, to train the acoustic model to align and predict one of multiple pronunciation candidates. Experimental results show that the proposed method improves ASR accuracy for Korean L2 speech by training solely on L1 speech data.
arXiv Detail & Related papers (2023-06-05T01:55:33Z)
ComSL: A Composite Speech-Language Model for End-to-End Speech-to-Text Translation [79.66359274050885]
We present ComSL, a speech-language model built atop a composite architecture of public pretrained speech-only and language-only models. Our approach has demonstrated effectiveness in end-to-end speech-to-text translation tasks.
arXiv Detail & Related papers (2023-05-24T07:42:15Z)
Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec Language Modeling [92.55131711064935]
We propose a cross-lingual neural language model, VALL-E X, for cross-lingual speech synthesis. VALL-E X inherits strong in-context learning capabilities and can be applied for zero-shot cross-lingual text-to-speech synthesis and zero-shot speech-to-speech translation tasks. It can generate high-quality speech in the target language via just one speech utterance in the source language as a prompt while preserving the unseen speaker's voice, emotion, and acoustic environment.
arXiv Detail & Related papers (2023-03-07T14:31:55Z)
YACLC: A Chinese Learner Corpus with Multidimensional Annotation [45.304130762057945]
We construct a large-scale, multidimensional annotated Chinese learner corpus. By analyzing the original sentences and annotations in the corpus, we found that YACLC has a considerable size and very high annotation quality.
arXiv Detail & Related papers (2021-12-30T13:07:08Z)
Semi-supervised transfer learning for language expansion of end-to-end speech recognition models to low-resource languages [19.44975351652865]
We propose a three-stage training methodology to improve the speech recognition accuracy of low-resource languages. We leverage a well-trained English model, unlabeled text corpus, and unlabeled audio corpus using transfer learning, TTS augmentation, and SSL respectively. Overall, our two-pass speech recognition system with a Monotonic Chunkwise Attention (MoA) in the first pass achieves a WER reduction of 42% relative to the baseline.
arXiv Detail & Related papers (2021-11-19T05:09:16Z)
Towards Language Modelling in the Speech Domain Using Sub-word Linguistic Units [56.52704348773307]
We propose a novel LSTM-based generative speech LM based on linguistic units including syllables and phonemes. With a limited dataset, orders of magnitude smaller than that required by contemporary generative models, our model closely approximates babbling speech. We show the effect of training with auxiliary text LMs, multitask learning objectives, and auxiliary articulatory features.
arXiv Detail & Related papers (2021-10-31T22:48:30Z)
Kosp2e: Korean Speech to English Translation Corpus [11.44330742875498]
We introduce kosp2e, a corpus that allows Korean speech to be translated into English text in an end-to-end manner. We adopt open license speech recognition corpus, translation corpus, and spoken language corpora to make our dataset freely available to the public.
arXiv Detail & Related papers (2021-07-06T20:34:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.