135 citations · 192 across the 8 of their papers we have counts for
9 papers
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…
DHASP: Differentiable Hearing Aid Speech Processing
Zehai Tu, Ning Ma, Jon Barker
Hearing aids are expected to improve speech intelligibility for listeners with hearing impairment. An appropriate amplification fitting tuned for the listener's hearing disability…
Robust Binaural Localization of a Target Sound Source by Combining Spectral Source Models and Deep Neural Networks
Ning Ma, Jose A. Gonzalez, Guy J. Brown
Despite there being clear evidence for top-down (e.g., attentional) effects in biological spatial hearing, relatively few machine hearing systems exploit top-down model-based knowl…
Exploiting Deep Neural Networks and Head Movements for Robust Binaural Localisation of Multiple Sources in Reverberant Environments
Ning Ma, Tobias May, Guy J. Brown
This paper presents a novel machine-hearing system that exploits deep neural networks (DNNs) and head movements for robust binaural localisation of multiple sources in reverberant…
Deep Learning Features for Robust Detection of Acoustic Events in Sleep-Disordered Breathing
Hector E. Romero, Ning Ma, Guy J. Brown +2
Sleep-disordered breathing (SDB) is a serious and prevalent condition, and acoustic analysis via consumer devices (e.g. smartphones) offers a low-cost solution to screening for it.…
End-to-end Binaural Sound Localisation from the Raw Waveform
Paolo Vecchiotti, Ning Ma, Stefano Squartini +1
A novel end-to-end binaural sound localisation approach is proposed which estimates the azimuth of a sound source directly from the waveform. Instead of employing hand-crafted feat…