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

By Ladan Baghai-Ravary

Automatic Speech sign research for medical analysis and overview of Speech issues provides a survey of tools designed to help clinicians within the analysis and tracking of speech issues corresponding to dysarthria and dyspraxia, with an emphasis at the sign processing options, statistical validity of the consequences provided within the literature, and the appropriateness of equipment that don't require really expert gear, carefully managed recording techniques or hugely expert group of workers to interpret effects.

Such innovations supply the promise of an easy and comparatively cheap, but goal, overview of a variety of health conditions, which might be of serious price to clinicians. the precise state of affairs could commence with the gathering of examples of the consumers’ speech, both over the telephone or utilizing transportable recording units operated via non-specialist nursing employees.

The recordings may then be analyzed before everything to help analysis of stipulations, and in this case to observe the consumers’ development and reaction to therapy. The automation of this method may let extra widespread and commonplace tests to be played, in addition to delivering better objectivity.

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DC removal from the log power spectrum). Their analysis used a fixed 46 ms (2048 samples) window length. The corresponding results for CPP gave a similar 71 % correlation. These figures might have been more informative if they had calculated the Spearman rank correlation coefficient, rather than simple linear correlation, because the ‘‘best fit’’ straight line to their data clearly did not pass through the origin of the graph, and there is no obvious reason to assume that any correlation with human ratings would be linear in any case.

2012) looked at sustained vowels using a dysphonia measure subset taken from 132 originally computed dysphonia measures, and achieved 99 % discrimination between Parkinson’s Disease and normal speech. The measures included many variants of jitter, shimmer and signal-to-noise ratio related measures. They compared four different feature selection algorithms to identify robust, parsimonious dysphonia measure subsets, and evaluated the out-of-sample performance of those subsets using Random Forests (RFs) and Support Vector Machines (SVMs) for classification.

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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