27 citations · 57 across the 16 of their papers we have counts for
8 papers · 1 filter
Investigating the Emergent Audio Classification Ability of ASR Foundation Models
Rao Ma, Adian Liusie, Mark J. F. Gales +1
Text and vision foundation models can perform many tasks in a zero-shot setting, a desirable property that enables these systems to be applied in general and low-resource settings.…
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…
Zero-shot Audio Topic Reranking using Large Language Models
Mengjie Qian, Rao Ma, Adian Liusie +3
Multimodal Video Search by Examples (MVSE) investigates using video clips as the query term for information retrieval, rather than the more traditional text query. This enables far…
Adapting an ASR Foundation Model for Spoken Language Assessment
Rao Ma, Mengjie Qian, Mark J. F. Gales +1
A crucial part of an accurate and reliable spoken language assessment system is the underlying ASR model. Recently, large-scale pre-trained ASR foundation models such as Whisper ha…
Can Generative Large Language Models Perform ASR Error Correction?
Rao Ma, Mengjie Qian, Potsawee Manakul +2
ASR error correction is an interesting option for post processing speech recognition system outputs. These error correction models are usually trained in a supervised fashion using…
Analysis of the Cambridge Multiple-Choice Questions Reading Dataset with a Focus on Candidate Response Distribution
Adian Liusie, Vatsal Raina, Andrew Mullooly +2
Multiple choice exams are widely used to assess candidates across a diverse range of domains and tasks. To moderate question quality, newly proposed questions often pass through pr…
Adapting an Unadaptable ASR System
Rao Ma, Mengjie Qian, Mark J. F. Gales +1
As speech recognition model sizes and training data requirements grow, it is increasingly common for systems to only be available via APIs from online service providers rather than…
N-best T5: Robust ASR Error Correction using Multiple Input Hypotheses and Constrained Decoding Space
Rao Ma, Mark J. F. Gales, Kate M. Knill +1
Error correction models form an important part of Automatic Speech Recognition (ASR) post-processing to improve the readability and quality of transcriptions. Most prior works use…