Edinburgh Research Explorer

Evaluation of objective measures for intelligibility prediction of HMM-based synthetic speech in noise

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

Related Edinburgh Organisations

Documents

  • Download as Adobe PDF

    Rights statement: © Valentini-Botinhao, C., Yamagishi, J., & King, S. (2011). Evaluation of objective measures for intelligibility prediction of HMM-based synthetic speech in noise. In Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on. (pp. 5112-5115). 10.1109/ICASSP.2011.5947507

    Submitted manuscript, 199 KB, PDF document

Original languageEnglish
Title of host publicationAcoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on
Pages5112-5115
Number of pages4
DOIs
Publication statusPublished - 1 May 2011

Abstract

In this paper we evaluate four objective measures of speech with regards to intelligibility prediction of synthesized speech in diverse noisy situations. We evaluated three intelligibility measures, the Dau measure, the glimpse proportion and the Speech Intelligibility Index (SII) and a quality measure, the Perceptual Evaluation of Speech Quality (PESQ). For the generation of synthesized speech we used a state of the art HMM-based speech synthesis system. The noisy conditions comprised four additive noises. The measures were compared with subjective intelligibility scores obtained in listening tests. The results show the Dau and the glimpse measures to be the best predictors of intelligibility, with correlations of around 0.83 to subjective scores. All measures gave less accurate predictions of intelligibility for synthetic speech than have previously been found for natural speech; in particular the SII measure. In additional experiments, we processed the synthesized speech by an ideal binary mask before adding noise. The Glimpse measure gave the most accurate intelligibility predictions in this situation.

Download statistics

No data available

ID: 2076787