1 citations · 1 across the 22 of their papers we have counts for
22 papers
Flexible and Interpretable Accent Distance Measurements
Charles McGhee, Mark J. F. Gales, Kate M. Knill
Determining the differences between two speakers' accents is a fundamental task in linguistics and speech technology research. The methodology used to measure these differences dep…
Controlling Implicit Shortcut Reliance in L2 Spoken English Auto-markers
Shilin Gao, Mark J. F. Gales, Kate M. Knill
Increasingly, speech and language processing tasks take either audio or text directly rather than extracting features from these as the input to the classifier or regressor. Often…
Data Augmentation for L2 English Speaking Assessment using TTS
Stefano Bannò, Penny Karanasou, Mengjie Qian +2
Automated assessment of second language (L2) speaking proficiency requires substantial annotated speech data, which are scarce compared to written learner corpora. We investigate w…
To Be Multimodal or Not to Be: Query-Adaptive Audio-Visual Person Retrieval via Active Modality Detection
Erfan Loweimi, Mengjie Qian, Kate Knill +7
When retrieving a person from a video archive by voice and face, should the system be multimodal or not? In real-world broadcast archives, unlike curated benchmarks, a target may b…
Towards Self-Referential Analytic Assessment: A Profile-Based Approach to L2 Writing Evaluation with LLMs
Stefano Bannò, Kate Knill, Mark Gales
Automated essay scoring (AES) research often relies on rank-based correlation metrics to validate analytic assessment. However, such metrics obscure both intrinsic intercorrelation…
The Impact of Editorial Intervention on Detecting Native Language Traces
Ahmet Yavuz Uluslu, Mark Gales, Kate Knill +1
Native Language Identification (NLI) is the task of determining an author's native language (L1) from their non-native writing. With the advent of human-AI co-authorship, learner t…
Exploiting the English Grammar Profile for L2 grammatical analysis with LLMs
Stefano Bannò, Penny Karanasou, Kate Knill +1
Evaluating the grammatical competence of second language (L2) learners is essential both for providing targeted feedback and for assessing proficiency. To achieve this, we propose…
Who can we trust? LLM-as-a-jury for Comparative Assessment
Mengjie Qian, Guangzhi Sun, Mark J. F. Gales +1
Large language models (LLMs) are increasingly applied as automatic evaluators for natural language generation assessment often using pairwise comparative judgements. Existing appro…
Detect, Attend and Extract: Keyword Guided Target Speaker Extraction
Haoyu Li, Yu Xi, Yidi Jiang +5
Target speaker extraction (TSE) aims to extract the speech of a target speaker from mixtures containing multiple competing speakers. Conventional TSE systems predominantly rely on…
Data Augmentation for Spoken Grammatical Error Correction
Penny Karanasou, Mengjie Qian, Stefano Bannò +2
While there exist strong benchmark datasets for grammatical error correction (GEC), high-quality annotated spoken datasets for Spoken GEC (SGEC) are still under-resourced. In this…
Natural Language-based Assessment of L2 Oral Proficiency using LLMs
Stefano Bannò, Rao Ma, Mengjie Qian +3
Natural language-based assessment (NLA) is an approach to second language assessment that uses instructions - expressed in the form of can-do descriptors - originally intended for…
End-to-End Spoken Grammatical Error Correction
Mengjie Qian, Rao Ma, Stefano Bannò +2
Grammatical Error Correction (GEC) and feedback play a vital role in supporting second language (L2) learners, educators, and examiners. While written GEC is well-established, spok…
Training Articulatory Inversion Models for Interspeaker Consistency
Charles McGhee, Mark J. F. Gales, Kate M. Knill
Acoustic-to-Articulatory Inversion (AAI) attempts to model the inverse mapping from speech to articulation. Exact articulatory prediction from speech alone may be impossible, as sp…
Exploiting the English Vocabulary Profile for L2 word-level vocabulary assessment with LLMs
Stefano Bannò, Kate Knill, Mark Gales
Vocabulary use is a fundamental aspect of second language (L2) proficiency. To date, its assessment by automated systems has typically examined the context-independent, or part-of-…
Assessment of L2 Oral Proficiency using Speech Large Language Models
Rao Ma, Mengjie Qian, Siyuan Tang +3
The growing population of L2 English speakers has increased the demand for developing automatic graders for spoken language assessment (SLA). Historically, statistical models, text…
Scaling and Prompting for Improved End-to-End Spoken Grammatical Error Correction
Mengjie Qian, Rao Ma, Stefano Bannò +2
Spoken Grammatical Error Correction (SGEC) and Feedback (SGECF) are crucial for second language learners, teachers and test takers. Traditional SGEC systems rely on a cascaded pipe…
Universal Acoustic Adversarial Attacks for Flexible Control of Speech-LLMs
Rao Ma, Mengjie Qian, Vyas Raina +2
The combination of pre-trained speech encoders with large language models has enabled the development of speech LLMs that can handle a wide range of spoken language processing task…
Unlearning vs. Obfuscation: Are We Truly Removing Knowledge?
Guangzhi Sun, Potsawee Manakul, Xiao Zhan +1
Unlearning has emerged as a critical capability for large language models (LLMs) to support data privacy, regulatory compliance, and ethical AI deployment. Recent techniques often…
Speaker Retrieval in the Wild: Challenges, Effectiveness and Robustness
Erfan Loweimi, Mengjie Qian, Kate Knill +1
There is a growing abundance of publicly available or company-owned audio/video archives, highlighting the increasing importance of efficient access to desired content and informat…
Speak & Improve Corpus 2025: an L2 English Speech Corpus for Language Assessment and Feedback
Kate Knill, Diane Nicholls, Mark J. F. Gales +2
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 te…
Speak & Improve Challenge 2025: Tasks and Baseline Systems
Mengjie Qian, Kate Knill, Stefano Banno +4
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 chall…
ASR Error Correction using Large Language Models
Rao Ma, Mengjie Qian, Mark Gales +1
Error correction (EC) models play a crucial role in refining Automatic Speech Recognition (ASR) transcriptions, enhancing the readability and quality of transcriptions. Without req…