Can Objective Measures Predict the Intelligibility of Modified HMM-based Synthetic Speech in Noise?

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Synthetic speech can be modified to improve intelligibility in noise. In order to perform modifications automatically, it would be useful to have an objective measure that could predict the intelligibility of modified synthetic speech for human listeners. We analysed the impact on intelligibility - and on how well objective measures predict it -“ when we separately modify speaking rate, fundamental frequency, line spectral pairs and spectral peaks. Shifting LSPs can increase intelligibility for human listeners; other modifications had weaker effects. Among the objective measures we evaluated, the Dau model and the Glimpse proportion were the best predictors of human performance.
Original languageEnglish
Title of host publicationInterspeech 2011
Subtitle of host publication12th Annual Conference of the International Speech Communication Association
PublisherInternational Speech Communication Association
Pages1837-1840
Number of pages4
ISBN (Print)1990-9772
Publication statusPublished - 1 Aug 2011

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