NDLI: Automatic intelligibility assessment of dysarthric speech using phonologically-structured sparse linear model

Content Provider	ACM Digital Library
Author	Kim, Hoirin Kim, Myung Jong Kim, Younggwan
Copyright Year	2016
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.
Starting Page	694
Ending Page	704
Page Count	11
File Format	PDF
ISSN	23299290
e-ISSN	23299304
DOI	10.1109/TASLP.2015.2403619
Volume Number	23
Issue Number	4
Journal	IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-04-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Dysarthria Pronunciation confusion network Speech intelligibility assessment Structured sparse model Weighted finite state transducer (WFST)
Content Type	Text
Resource Type	Article
Subject	Instrumentation Computational Mathematics Signal Processing Electrical and Electronic Engineering Acoustics and Ultrasonics Speech and Hearing Media Technology

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