Speak & Improve Challenge 2025: Tasks and Baseline Systems
- URL: http://arxiv.org/abs/2412.11985v2
- Date: Tue, 17 Dec 2024 16:47:49 GMT
- Title: Speak & Improve Challenge 2025: Tasks and Baseline Systems
- Authors: Mengjie Qian, Kate Knill, Stefano Banno, Siyuan Tang, Penny Karanasou, Mark J. F. Gales, Diane Nicholls,
- Abstract summary: "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)
- Score: 28.877872578497854
- License:
- Abstract: 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.
Related papers
- Speak & Improve Corpus 2025: an L2 English Speech Corpus for Language Assessment and Feedback [28.53752312060031]
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.
arXiv Detail & Related papers (2024-12-16T17:07:26Z) - 1-800-SHARED-TASKS @ NLU of Devanagari Script Languages: Detection of Language, Hate Speech, and Targets using LLMs [0.0]
This paper presents a detailed system description of our entry for the CHiPSAL 2025 shared task.
We focus on language detection, hate speech identification, and target detection in Devanagari script languages.
arXiv Detail & Related papers (2024-11-11T10:34:36Z) - Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition [110.8431434620642]
We introduce the generative speech transcription error correction (GenSEC) challenge.
This challenge comprises three post-ASR language modeling tasks: (i) post-ASR transcription correction, (ii) speaker tagging, and (iii) emotion recognition.
We discuss insights from baseline evaluations, as well as lessons learned for designing future evaluations.
arXiv Detail & Related papers (2024-09-15T16:32:49Z) - Summary of the DISPLACE Challenge 2023 -- DIarization of SPeaker and
LAnguage in Conversational Environments [28.618333018398122]
In multi-lingual societies, where multiple languages are spoken in a small geographic vicinity, informal conversations often involve mix of languages.
Existing speech technologies may be inefficient in extracting information from such conversations, where the speech data is rich in diversity with multiple languages and speakers.
The DISPLACE challenge constitutes an open-call for evaluating and bench-marking the speaker and language diarization technologies on this challenging condition.
arXiv Detail & Related papers (2023-11-21T12:23:58Z) - Findings of the 2023 ML-SUPERB Challenge: Pre-Training and Evaluation
over More Languages and Beyond [89.54151859266202]
The 2023 Multilingual Speech Universal Performance Benchmark (ML-SUPERB) Challenge expands upon the acclaimed SUPERB framework.
The challenge garnered 12 model submissions and 54 language corpora, resulting in a comprehensive benchmark encompassing 154 languages.
The findings indicate that merely scaling models is not the definitive solution for multilingual speech tasks.
arXiv Detail & Related papers (2023-10-09T08:30:01Z) - 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) - SpokenWOZ: A Large-Scale Speech-Text Benchmark for Spoken Task-Oriented
Dialogue Agents [72.42049370297849]
SpokenWOZ is a large-scale speech-text dataset for spoken TOD.
Cross-turn slot and reasoning slot detection are new challenges for SpokenWOZ.
arXiv Detail & Related papers (2023-05-22T13:47:51Z) - Pretraining Approaches for Spoken Language Recognition: TalTech
Submission to the OLR 2021 Challenge [0.0]
The paper is based on our submission to the Oriental Language Recognition 2021 Challenge.
For the constrained track, we first trained a Conformer-based encoder-decoder model for multilingual automatic speech recognition.
For the unconstrained task, we relied on both externally available pretrained models as well as external data.
arXiv Detail & Related papers (2022-05-14T15:17:08Z) - Auto-KWS 2021 Challenge: Task, Datasets, and Baselines [63.82759886293636]
Auto-KWS 2021 challenge calls for automated machine learning (AutoML) solutions to automate the process of applying machine learning to a customized keyword spotting task.
The challenge focuses on the problem of customized keyword spotting, where the target device can only be awakened by an enrolled speaker with his specified keyword.
arXiv Detail & Related papers (2021-03-31T14:56:48Z) - VoxSRC 2020: The Second VoxCeleb Speaker Recognition Challenge [99.82500204110015]
We held the second installment of the VoxCeleb Speaker Recognition Challenge in conjunction with Interspeech 2020.
The goal of this challenge was to assess how well current speaker recognition technology is able to diarise and recognize speakers in unconstrained or in the wild' data.
This paper outlines the challenge, and describes the baselines, methods used, and results.
arXiv Detail & Related papers (2020-12-12T17:20:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.