Abstract
Some of the most common forms of speech impairment are summarised here, in terms of their main distinctive acoustic and temporal characteristics (rhythm, intonation, etc.). Where they offer significant advantages, we also mention some non-acoustic methods for assessment and diagnosis. The speech disorders considered in this book are neurological in origin: primarily dysphonia, dysprosody, dysarthria and apraxia of speech, but we also mention some considerations relevant to the diagnosis of stuttering, Parkinson’s disease and even schizophrenia. It is suggested that the tasks of assessing severity of a condition, and of differential diagnosis, need not use the same acoustic features, and indeed there may be significant advantages in using complementary features and procedures for the two tasks.
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References
Bhogal SK, Teasell R, Speechley M (2003) Intensity of aphasia therapy, impact on recovery. Stroke 34:987–993. doi:10.1161/01.str.0000062343.64383.d0
Cera ML, Ortiz KZ (2010) Phonological analysis of substitution errors of patients with apraxia of speech. Dementia Neuropsychol 4(1):58–62
Chomsky N, Halle M (1968) The sound pattern of english. Harper & Row, New York
Cummings L (2008) Clinical linguistics. Edinburgh University Press Ltd, Edinburgh. ISBN 978 0 7486 2077 7
Davis S, Howell P, Rustin L (2000) A multivariate approach to diagnosis and prediction of therapy outcome with children who stutter; the social status of the child who stutters. In: Baker KL, Rustin L, Cook F (eds) Proceedings of fifth oxford dysfluency conference, pp 32–41
Fletcher SG (1972) Time-by-count measurement of diadochokinetic syllable rate. J Speech Hearing Res 15:763–770
Masaki A (2010) Optimizing acoustic and perceptual assessment of voice quality in children with vocal nodules. PhD thesis, Harvard-MIT Health Sciences and Technology
Middag C, Martens J-P, van Nuffelen G, de Bodt M (2009) Automated intelligibility assessment of pathological speech using phonological features. EURASIP J Adv Signal Process. doi:10.1155/2009/629030
Ogar J, Slama H, Dronkers N, Amici S, Gorno-Tempini ML (2005) Apraxia of speech: an overview. Neurocase 11(6):427–432
Portnoy RA, Aronson AE (1982) Diadochokinetic syllable rate and regularity in normal and in spastic and ataxic dysarthric subjects. J Speech Hearing Disorders 47:324–328
Reetz H (1989) A fast expert program for pitch extraction. Proc Eurospeech 1:476–479
van Santen JPH, Prud’hommeaux ET, Black LM (2009) Automated assessment of prosody production. Speech Commun 51(11):1082–1097. doi:10.1016/j.specom.2009.04.007
Shriberg LD, Hosom J-P, Green JR (2004) Diagnostic assessment of childhood apraxia of speech using automatic speech recognition (ASR) systems. J Med Speech Lang Pathol 12(4):167–171
Thoonen GHJ (1998) Developmental apraxia of speech in children—quantitative assessment of speech characteristics. Thesis, University of Nijmegen. ISBN 90-9011330-4
Yorkston KM, Beukelman DR, Strand EA, Bell KR (1999) Management of motor speech disorders in children and adults, 2nd edn. PRO-ED, Austin
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Baghai-Ravary, L., Beet, S.W. (2013). Acoustic Effects of Speech Impairment. In: Automatic Speech Signal Analysis for Clinical Diagnosis and Assessment of Speech Disorders. SpringerBriefs in Electrical and Computer Engineering(). Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4574-6_3
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DOI: https://doi.org/10.1007/978-1-4614-4574-6_3
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