Automatic Speech Signal Analysis for Clinical Diagnosis and by Ladan Baghai-Ravary

By Ladan Baghai-Ravary

Automatic Speech sign research for scientific prognosis and evaluate of Speech problems provides a survey of equipment designed to help clinicians within the prognosis and tracking of speech problems equivalent to dysarthria and dyspraxia, with an emphasis at the sign processing innovations, statistical validity of the consequences offered within the literature, and the appropriateness of equipment that don't require really good gear, carefully managed recording techniques or hugely expert team of workers to interpret effects.

Such strategies supply the promise of an easy and low in cost, but goal, evaluation of quite a number health conditions, which might be of serious price to clinicians. the suitable state of affairs might start with the gathering of examples of the consumers’ speech, both over the telephone or utilizing moveable recording units operated by means of non-specialist nursing employees.

The recordings may then be analyzed at first to help prognosis of stipulations, and for that reason to watch the consumers’ development and reaction to therapy. The automation of this approach might enable extra common and average exams to be played, in addition to supplying better objectivity.

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

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Since the idea was first proposed, there have been significant developments in the area of speech recognition based on representations with ‘‘missing data’’ (Barker et al. 2000; Cooke 2006; Joshi and Guan 2006, for example), and this approach appears to offer the greatest hope for robust analysis of this type of realworld signal. However, this is still far from being a ‘‘solved problem’’. The most difficult part of the missing-data approach is normally the identification of exactly which time–frequency regions are missing or corrupted.

EURASIP J Appl Signal Process 2001:275–284 Alpan A, Schoentgen J, Maryn Y, Grenez F (2010) Automatic perceptual categorization of disordered connected speech. In: Proceedings of 11th annual conference on international speech communication association 2010, pp 2574–2577 References 35 Bakker K, Arkebauer H, Boutsen F (1993) Computer-assisted determination of diadochokinetic rate and variability. Mini-seminar presented at annual convention of the American Speech and Hearing Association (ASHA). pdf.

Many papers, including Hariharan et al. (2010) have discussed the commonlyused speech analysis and classification tools, including multi-layer perceptron (MLP—probably the most widely used form of ANN), learning vector quantisation (LVQ), hidden Markov model (HMM), linear discriminant analysis (LDA), 46 5 Established Methods Gaussian mixture model (GMM) and K-nearest neighbour (KNN) classifiers. The general superiority of any specific technique is, however, rarely clear-cut. One approach may appear to have clear benefits in one situation or one application, but may be inferior in another.

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