no code implementations • 20 Oct 2023 • 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 Challenge (CPC2).
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 19 Oct 2023 • Wanli Sun, Zehai Tu, Anton Ragni
It also describes how sampling from EBMs can be performed using Langevin Markov Chain Monte-Carlo (MCMC).
1 code implementation • 8 Apr 2022 • 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.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
1 code implementation • 8 Apr 2022 • 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.
no code implementations • 8 Apr 2022 • Zehai Tu, Jack Deadman, Ning Ma, Jon Barker
End-to-end models have achieved significant improvement on automatic speech recognition.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 15 Mar 2021 • Zehai Tu, Ning Ma, Jon Barker
In this paper, we explore an alternative approach to finding the optimal fitting by introducing a hearing aid speech processing framework, in which the fitting is optimised in an automated way using an intelligibility objective function based on the HASPI physiological auditory model.
1 code implementation • 20 Nov 2017 • Avgoustinos Vouros, Tiago V. Gehring, Kinga Szydlowska, Artur Janusz, Mike Croucher, Katarzyna Lukasiuk, Witold Konopka, Carmen Sandi, Zehai Tu, Eleni Vasilaki
The Morris Water Maze is commonly used in behavioural neuroscience for the study of spatial learning with rodents.