5 citations · 7 across the 16 of their papers we have counts for
19 papers
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…
Bias Analysis of L2 Speaking Assessment Systems Using Concept Activation Vectors
Arya Labroo, Mengjie Qian, Kate Knill
Automatic speaking assessment systems are increasingly deployed in high-stakes settings to mark second language (L2) learners' speaking tests, making it critical to show that their…
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…
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…
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…
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…
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…
Learn and Don't Forget: Adding a New Language to ASR Foundation Models
Mengjie Qian, Siyuan Tang, Rao Ma +2
Foundation ASR models often support many languages, e.g. 100 languages in Whisper. However, there has been limited work on integrating an additional, typically low-resource, langua…
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…
Towards End-to-End Spoken Grammatical Error Correction
Stefano Bannò, Rao Ma, Mengjie Qian +2
Grammatical feedback is crucial for L2 learners, teachers, and testers. Spoken grammatical error correction (GEC) aims to supply feedback to L2 learners on their use of grammar whe…