Automatic Speech Signal Analysis for Clinical Diagnosis and Assessment of Speech Disorders

Automatic Speech Signal Analysis for Clinical Diagnosis and Assessment of Speech Disorders provides a survey of methods designed to aid clinicians in the diagnosis and monitoring of speech disorders such as dysarthria and dyspraxia, with an emphasis on the signal processing techniques, statistical validity of the results presented in the literature, and the appropriateness of methods that do not require specialized equipment, rigorously controlled recording procedures or highly skilled personnel to interpret results.

Such techniques offer the promise of a simple and cost-effective, yet objective, assessment of a range of medical conditions, which would be of great value to clinicians. The ideal scenario would begin with the collection of examples of the clients’ speech, either over the phone or using portable recording devices operated by non-specialist nursing staff.

The recordings could then be analyzed initially to aid diagnosis of conditions, and subsequently to monitor the clients’ progress and response to treatment. The automation of this process would allow more frequent and regular assessments to be performed, as well as providing greater objectivity.

• Markov Model
• Neural Networks
• Non-stationary signal models
• Signal processing techniques
• Speaker characterization
• Speech disorder assessment
• Speech production
• Speech recognition

• , Phonetics Laboratory, University of Oxford, Oxford, United Kingdom

  Ladan Baghai-Ravary

• Aculab plc, Milton Keynes, United Kingdom

  Steve W. Beet

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