Spoken Language Learning and Assessment

Datasets

Spoken

• The ISLE corpus of non-native spoken English (Menzel et al., 2000)
• The NICT JLE Corpus Exploiting the language learners’ speech database for research and education (Izumi et al., 2004)
• L2-ARCTIC: A Non-Native English Speech Corpus (Zhao et al., 2018)
• Overview of the 2019 Spoken CALL Shared Task (Baur et al., 2019)
• speechocean762: An Open-Source Non-native English Speech Corpus For Pronunciation Assessment (Zhang et al., 2021)
• Development and Administration of a Skype-based English Speaking Test in a Japanese High School (Kanzawa et al., 2022)
• A corpus of spontaneous L2 English speech for real-situation speaking assessment (Coulange et al., 2024)
• LearnerVoice: A Dataset of Non-Native English Learners’ Spontaneous Speech (Kim et al., 2024)

Written

• A New Dataset and Method for Automatically Grading ESOL Texts (Yannakoudakis et al., 2011)
• Automatic Linguistic Annotation of Large Scale L2 Databases: The EF-Cambridge Open Language Database (EFCamDat) (Geertzen et al., 2014)
• The BEA-2019 Shared Task on Grammatical Error Correction (Bryant et al., 2019)

Spoken & Written

• A new horizon in learner corpus studies: The aim of the ICNALE project (Ishikawa, 2011)
• Designing and compiling a learner corpus of written and spoken narratives: COREFL (Lozano et al., 2020)

Automated Speaking Assessment

• The Use of DBN-HMMs for Mispronunciation Detection and Diagnosis in L2 English to Support Computer-Aided Pronunciation Training (Qian et al., 2012)
• Using an Automated Content Scoring System for Spoken CALL Responses: The ETS submission for the Spoken CALL Challenge (Evanini et al., 2017)
• Incorporating Uncertainty into Deep Learning for Spoken Language Assessment (Malinin et al., 2017)
• Automated Speaking Assessment Using Language Technologies to Score Spontaneous Speech (Zechner & Evanini, 2019)
• Automated scoring of spontaneous speech from young learners of English using transformers (Wang et al., 2021)
• Transformer Based End-to-End Mispronunciation Detection and Diagnosis (Wu et al., 2021)
• Explore Wav2vec 2.0 for Mispronunciation Detection (Xu et al., 2021)
• View-Specific Assessment of L2 Spoken English (Banno et al., 2022)
• Automatic Pronunciation Assessment using Self-Supervised Speech Representation Learning (Kim et al., 2022)
• Assessment of L2 Oral Proficiency Using Self-Supervised Speech Representation Learning (Banno et al., 2023)
• Automatic Assessment of Conversational Speaking Tests (McKnight et al., 2023)

Spoken GED and GEC

• Automatic error detection in the Japanese learners’ English spoken data (Izumi et al., 2003)
• Impact of ASR Performance on Spoken Grammatical Error Detection (Lu et al., 2019)
• Automatic Grammatical Error Detection of Non-Native Spoken Learner English (Knill et al., 2019)
• Spoken Language ‘Grammatical Error Correction’ (Lu et al., 2020)
• On Assessing and Developing Spoken ’Grammatical Error Correction’ Systems (Lu et al., 2022)
• Towards End-to-End Spoken Grammatical Error Correction (Banno et al., 2024)

LLMs for Assessment and Feedback

• On the application of Large Language Models for language teaching and assessment technology (Caines et al., 2023)
• Exploring the potential of using an AI language model for automated essay scoring (Mizumoto & Eguchi, 2023)
• Rating Short L2 Essays on the CEFR Scale with GPT-4 (Yancey et al., 2023)
• Can GPT-4 do L2 analytic assessment? (Banno et al., 2024)
• Pronunciation Assessment with Multi-modal Large Language Models (Fu et al., 2024)
• EvalYaks: Instruction Tuning Datasets and LoRA Fine-tuned Models for Automated Scoring of CEFR B2 Speaking Assessment Transcripts (Scaria et al., 2024)

Validity and Reliability

• Building a validity argument for the Speaking test (Xu et al., 2020)
• Assessing L2 English speaking using automated scoring technology: examining automarker reliability (Xu et al., 2021)