97 citations · 217 across the 14 of their papers we have counts for
15 papers
Learning task-specific subspaces via interventional post-training of speech foundation models
Jack Cox, Jon Barker
Speech foundation models, pre-trained on large corpora of unlabelled speech data, produce general-purpose representations which are useful across tasks. However, these representati…
On monoaural speech enhancement for automatic recognition of real noisy speech using mixture invariant training
Jisi Zhang, Catalin Zorila, Rama Doddipatla +1
In this paper, we explore an improved framework to train a monoaural neural enhancement model for robust speech recognition. The designed training framework extends the existing mi…
Auditory-Based Data Augmentation for End-to-End Automatic Speech Recognition
Zehai Tu, Jack Deadman, Ning Ma +1
End-to-end models have achieved significant improvement on automatic speech recognition. One common method to improve performance of these models is expanding the data-space throug…
Unsupervised Uncertainty Measures of Automatic Speech Recognition for Non-intrusive Speech Intelligibility Prediction
Zehai Tu, Ning Ma, Jon Barker
Non-intrusive intelligibility prediction is important for its application in realistic scenarios, where a clean reference signal is difficult to access. The construction of many no…
Exploiting Hidden Representations from a DNN-based Speech Recogniser for Speech Intelligibility Prediction in Hearing-impaired Listeners
Zehai Tu, Ning Ma, Jon Barker
An accurate objective speech intelligibility prediction algorithms is of great interest for many applications such as speech enhancement for hearing aids. Most algorithms measures…
Optimising Hearing Aid Fittings for Speech in Noise with a Differentiable Hearing Loss Model
Zehai Tu, Ning Ma, Jon Barker
Current hearing aids normally provide amplification based on a general prescriptive fitting, and the benefits provided by the hearing aids vary among different listening environmen…