Disordered Speech Assessment Using Automatic Methods Based on Quantitative Measures
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Disordered Speech Assessment Using Automatic Methods Based on Quantitative Measures Lingyun Gu Computational NeuroEngineering Laboratory, Department of Electrical & Computer Engineering, University of Florida, Gainesville, FL 32611-6200, USA Email: [email protected]
John G. Harris Computational NeuroEngineering Laboratory, Department of Electrical & Computer Engineering, University of Florida, Gainesville, FL 32611-6200, USA Email: [email protected]
Rahul Shrivastav Department of Communication Sciences & Disorders, University of Florida, Gainesville, FL 32611, USA Email: [email protected]
Christine Sapienza Department of Communication Sciences & Disorders, University of Florida, Gainesville, FL 32611, USA Email: [email protected] Received 2 November 2003; Revised 6 August 2004 Speech quality assessment methods are necessary for evaluating and documenting treatment outcomes of patients suffering from degraded speech due to Parkinson’s disease, stroke, or other disease processes. Subjective methods of speech quality assessment are more accurate and more robust than objective methods but are time-consuming and costly. We propose a novel objective measure of speech quality assessment that builds on traditional speech processing techniques such as dynamic time warping (DTW) and the Itakura-Saito (IS) distortion measure. Initial results show that our objective measure correlates well with the more expensive subjective methods. Keywords and phrases: objective speech quality measures, subjective speech quality measures, pathology, anthropomorphic.
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INTRODUCTION
The accurate assessment of speech quality is a major research problem that has attracted attention in the field of speech communications for many years. The two major classes of methods employed in the assessment of speech quality are subjective and objective speech quality measures. Subjective quality measures are more accurate and robust since they are given by professional personnel who have received special assessment training, but they are necessarily time consuming and costly. On the contrary, objective quality measures, inspired by speech signal processing techniques, provide an efficient, economical alternative to subjective measures. Although it is not suggested to use objective quality measures to completely replace subjective measures, objective quality measures do show the strong ability to predict subjective quality measures and the results do correlate very
well with those produced by subjective quality measures [1]. Traditionally, objective measures have been used to evaluate speech after decoding and in the presence of noise. Currently, some pioneers have already developed some system protocols or algorithms to apply objective speech quality assessment into disordered speech analysis. Any meaningful quality assessment should be consistent with human responses and perception. Therefore, subjective measures naturally became the first choice to evaluate speech quality. Performance methods using subjective measures are based on a group of listen
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