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  • Research Article
  • Open Access

Automated Intelligibility Assessment of Pathological Speech Using Phonological Features

  • 1Email author,
  • 1,
  • 2 and
  • 2
EURASIP Journal on Advances in Signal Processing20092009:629030

  • Received: 31 October 2008
  • Accepted: 24 March 2009
  • Published:


It is commonly acknowledged that word or phoneme intelligibility is an important criterion in the assessment of the communication efficiency of a pathological speaker. People have therefore put a lot of effort in the design of perceptual intelligibility rating tests. These tests usually have the drawback that they employ unnatural speech material (e.g., nonsense words) and that they cannot fully exclude errors due to listener bias. Therefore, there is a growing interest in the application of objective automatic speech recognition technology to automate the intelligibility assessment. Current research is headed towards the design of automated methods which can be shown to produce ratings that correspond well with those emerging from a well-designed and well-performed perceptual test. In this paper, a novel methodology that is built on previous work (Middag et al., 2008) is presented. It utilizes phonological features, automatic speech alignment based on acoustic models that were trained on normal speech, context-dependent speaker feature extraction, and intelligibility prediction based on a small model that can be trained on pathological speech samples. The experimental evaluation of the new system reveals that the root mean squared error of the discrepancies between perceived and computed intelligibilities can be as low as 8 on a scale of 0 to 100.


  • Automatic Speech Recognition
  • Acoustic Model
  • Speech Sample
  • Pathological Speech
  • Nonsense Word

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Authors’ Affiliations

Department of Electronics and Information Systems, Ghent University, 9000 Ghent, Belgium
Antwerp University Hospital, University of Antwerp, 2650 Edegem, Belgium


© Catherine Middag et al. 2009

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.