45 citations · 48 across the 8 of their papers we have counts for
4 papers · 1 filter
Intelligibility prediction with a pretrained noise-robust automatic speech recognition model
Zehai Tu, Ning Ma, Jon Barker
This paper describes two intelligibility prediction systems derived from a pretrained noise-robust automatic speech recognition (ASR) model for the second Clarity Prediction Challe…
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…