1 citations · 1 across the 21 of their papers we have counts for
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Adaptive Fisher-Whitened Cross-Covariance for Low-Resource Speech Recognition
Asmee Mishra, Mengjie Qian, Brechtje Post +1
Adapting multilingual speech foundation models to low-resource languages remains difficult, especially for languages that are poorly represented during pre-training. While paramete…
Sequential Adapter Stacking for Cross-Lingual Low-Resource ASR
Thai Thi Thanh Thao Dang, Mengjie Qian, Kate Knill
Extending large-scale multilingual automatic speech recognition (ASR) models to low-resource languages remains challenging. Model performance is skewed toward high-resource languag…
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…
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…
Dual-Space Knowledge Distillation with Key-Query Matching for Large Language Models with Vocabulary Mismatch
Stella Eva Tsiapali, Cong-Thanh Do, Kate Knill
Large language models (LLMs) achieve state-of-the-art (SOTA) performance across language tasks, but are costly to deploy due to their size and resource demands. Knowledge Distillat…
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…
Effectiveness of Chain-of-Thought in Distilling Reasoning Capability from Large Language Models
Cong-Thanh Do, Rama Doddipatla, Kate Knill
Chain-of-Thought (CoT) prompting is a widely used method to improve the reasoning capability of Large Language Models (LLMs). More recently, CoT has been leveraged in Knowledge Dis…
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…
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…
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-…
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…
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…
Cross-Lingual Transfer Learning for Speech Translation
Rao Ma, Mengjie Qian, Yassir Fathullah +3
There has been increasing interest in building multilingual foundation models for NLP and speech research. This paper examines how to expand the speech translation capability of th…
Muting Whisper: A Universal Acoustic Adversarial Attack on Speech Foundation Models
Vyas Raina, Rao Ma, Charles McGhee +2
Recent developments in large speech foundation models like Whisper have led to their widespread use in many automatic speech recognition (ASR) applications. These systems incorpora…