97 citations · 152 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
The Use of Voice Source Features for Sung Speech Recognition
Gerardo Roa Dabike, Jon Barker
In this paper, we ask whether vocal source features (pitch, shimmer, jitter, etc) can improve the performance of automatic sung speech recognition, arguing that conclusions previou…
Time-Domain Speech Extraction with Spatial Information and Multi Speaker Conditioning Mechanism
Jisi Zhang, Catalin Zorila, Rama Doddipatla +1
In this paper, we present a novel multi-channel speech extraction system to simultaneously extract multiple clean individual sources from a mixture in noisy and reverberant environ…
On End-to-end Multi-channel Time Domain Speech Separation in Reverberant Environments
Jisi Zhang, Catalin Zorila, Rama Doddipatla +1
This paper introduces a new method for multi-channel time domain speech separation in reverberant environments. A fully-convolutional neural network structure has been used to dire…
Clarity: Machine Learning Challenges to Revolutionise Hearing Device Processing
Simone Graetzer, Michael Akeroyd, Jon P. Barker +5
In the Clarity project, we will run a series of machine learning challenges to revolutionise speech processing for hearing devices. Over five years, there will be three paired chal…