Detail publikačního výsledku

An Automatic Diagnosis and Assessment of Dysarthric Speech using Speech Disorder Specific Prosodic Features

VYAS, G.; DUTTA, M.; PŘINOSIL, J.; HARÁR, P.

Originální název

An Automatic Diagnosis and Assessment of Dysarthric Speech using Speech Disorder Specific Prosodic Features

Anglický název

An Automatic Diagnosis and Assessment of Dysarthric Speech using Speech Disorder Specific Prosodic Features

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

To diagnose and classify the dysarthria speech, speech language pathologist (SLP) conduct a listening test. On the basis of the scores given by listeners the dysarthria is diagnosed and assessed. The above mentioned method is costly, time consuming and not very accurate. Unlike the traditional method, this research proposes an automatic diagnosis and assessment of dysarthria. The aim of this paper is to diagnose and classify the severity of dysarthria. The speech disorder specific prosodic features are selected by using genetic algorithm. The diagnosis and assessment of dysarthria speech is done by support vector machines. During diagnosis the classification accuracy of 98% has been achieved. And 87% of the dysarthria speech utterances are correctly classified. The standard UASPEECH database has been used in this work.

Anglický abstrakt

To diagnose and classify the dysarthria speech, speech language pathologist (SLP) conduct a listening test. On the basis of the scores given by listeners the dysarthria is diagnosed and assessed. The above mentioned method is costly, time consuming and not very accurate. Unlike the traditional method, this research proposes an automatic diagnosis and assessment of dysarthria. The aim of this paper is to diagnose and classify the severity of dysarthria. The speech disorder specific prosodic features are selected by using genetic algorithm. The diagnosis and assessment of dysarthria speech is done by support vector machines. During diagnosis the classification accuracy of 98% has been achieved. And 87% of the dysarthria speech utterances are correctly classified. The standard UASPEECH database has been used in this work.

Klíčová slova

Dysarthria speech; diagnosis; assessment; speech disorder; prosodic features; support vector machines

Klíčová slova v angličtině

Dysarthria speech; diagnosis; assessment; speech disorder; prosodic features; support vector machines

Autoři

VYAS, G.; DUTTA, M.; PŘINOSIL, J.; HARÁR, P.

Rok RIV

2017

Vydáno

27.06.2016

Místo

Vienna, Austria

ISBN

978-1-5090-1287-9

Kniha

2016 39th International Conference on Telecommunications and Signal Processing (TSP)

Strany od

515

Strany do

518

Strany počet

4

URL

https://ieeexplore.ieee.org/document/7760933

BibTex

@inproceedings{BUT127569,
  author="Garima {Vyas} and Malay Kishore {Dutta} and Jiří {Přinosil} and Pavol {Harár}",
  title="An Automatic Diagnosis and Assessment of Dysarthric Speech using Speech Disorder Specific Prosodic Features",
  booktitle="2016 39th International Conference on Telecommunications and Signal Processing (TSP)",
  year="2016",
  pages="515--518",
  address="Vienna, Austria",
  doi="10.1109/TSP.2016.7760933",
  isbn="978-1-5090-1287-9",
  url="https://ieeexplore.ieee.org/document/7760933"
}