Automatic Intelligibility Assessment of Dysarthric Speech Using Phonologically-Structured Sparse Linear Model

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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.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2015-04
Language
English
Article Type
Article
Keywords

ARTICULATION ERRORS; CEREBRAL-PALSY; RECOGNITION; DISORDERS; ALGORITHM; SELECTION; SPEAKERS

Citation

IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, v.23, no.4, pp.694 - 704

ISSN
2329-9290
DOI
10.1109/TASLP.2015.2403619
URI
http://hdl.handle.net/10203/198286
Appears in Collection
EE-Journal Papers(저널논문)
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