C
Catherine Mayo
Researcher at University of Edinburgh
Publications - 28
Citations - 838
Catherine Mayo is an academic researcher from University of Edinburgh. The author has contributed to research in topics: Speech perception & Intelligibility (communication). The author has an hindex of 13, co-authored 28 publications receiving 779 citations.
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The Blizzard Challenge 2008
TL;DR: The Blizzard Challenge 2008 was the fourth annual Blizzard Challenge as mentioned in this paper, where participants were asked to build two voices from a UK English corpus and one voice from a Man-Darin Chinese corpus.
Journal ArticleDOI
Evaluating the intelligibility benefit of speech modifications in known noise conditions
Martin Cooke,Catherine Mayo,Cassia Valentini-Botinhao,Yannis Stylianou,Bastian Sauert,Yan Tang +5 more
TL;DR: The current study compares the benefits of speech modification algorithms in a large-scale speech intelligibility evaluation and quantifies the equivalent intensity change, defined as the amount in decibels that unmodified speech would need to be adjusted by in order to achieve the same intelligibility as modified speech.
Statistical analysis of the Blizzard Challenge 2007 listening test results
TL;DR: A listening test for the third Blizzard Challenge was conducted in 2007 as mentioned in this paper, where participants build voices from a common dataset and a large listening test is conducted which allows comparison of systems in terms of naturalness and intelligibility.
Journal ArticleDOI
Adult–child differences in acoustic cue weighting are influenced by segmental context: Children are not always perceptually biased toward transitions
Catherine Mayo,Alice Turk +1 more
TL;DR: Results suggest that children do not always show a bias towards vowel-formant transitions, but that cue weighting can differ according to segmental context, and possibly the physical distinctiveness of available acoustic cues.
Proceedings ArticleDOI
Intelligibility-enhancing speech modifications: the Hurricane Challenge
TL;DR: Surprisingly, for most conditions the largest gains were observed for noise-independent algorithms, suggesting that performance in this task can be further improved by exploiting information in the masking signal.