27 citations · 49 across the 7 of their papers we have counts for
7 papers
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…
Towards spoken dialect identification of Irish
Liam Lonergan, Mengjie Qian, Neasa Ní Chiaráin +2
The Irish language is rich in its diversity of dialects and accents. This compounds the difficulty of creating a speech recognition system for the low-resource language, as such a…
Towards dialect-inclusive recognition in a low-resource language: are balanced corpora the answer?
Liam Lonergan, Mengjie Qian, Neasa Ní Chiaráin +2
ASR systems are generally built for the spoken 'standard', and their performance declines for non-standard dialects/varieties. This is a problem for a language like Irish, where th…
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…
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…