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• W2040972403 abstract "This paper presents a new method for automatically assessing the speech intelligibility of patients with dysarthria, which is a motor speech disorder impeding the physical production of speech. The proposed method consists of two main steps: feature representation and prediction. In the feature representation step, the speech utterance is converted into a phone sequence using an automatic speech recognition technique and is then aligned with a canonical phone sequence from a pronunciation dictionary using a weighted finite state transducer to capture the pronunciation mappings such as match, substitution, and deletion. The histograms of the pronunciation mappings on a pre-defined word set are used for features. Next, in the prediction step, a structured sparse linear model incorporated with phonological knowledge that simultaneously addresses phonologically structured sparse feature selection and intelligibility prediction is proposed. Evaluation of the proposed method on a database of 109 speakers consisting of 94 dysarthric and 15 control speakers yielded a root mean square error of 8.14 compared to subjectively rated scores in the range of 0 to 100. This is a promising performance in which the system can be successfully applied to help speech therapists in diagnosing the degree of speech disorder." @default.
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• W2040972403 date "2015-04-01" @default.
• W2040972403 modified "2023-09-27" @default.
• W2040972403 title "Automatic Intelligibility Assessment of Dysarthric Speech Using Phonologically-Structured Sparse Linear Model" @default.
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• W2040972403 doi "https://doi.org/10.1109/taslp.2015.2403619" @default.
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