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AUTOMATED ASSESSMENT FOR THE THERAPY SUCCESS OF FOREIGN ACCENT SYNDROME: Based on Emotional Temperature
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Context. Foreign Accent Syndrome is a rare neurological disorder, where among other symptoms of the patient’s emotional speech is affected. As FAS is one of the mildest speech disorders, there has not been much research done on the cost-effective biomarkers which reflect recovery of competences speech.

Objectives. In this pilot study, we implement the Emotional Temperature biomarker and check its validity for assessing the FAS. We compare the results of implemented biomarker with another biomarker based on the global distances for FAS and identify the better one.

Methods. To reach the objective, the emotional speech data of two patients at different phases of the treatment are considered. After preprocessing, experiments are performed on various window sizes and the observed correctly classified instances in automatic recognition are used to calculate Emotional temperature. Further, we use the better biomarker for tracking the recovery in the patient’s speech.

Results. The Emotional temperature of the patient is calculated and compared with the ground truth and with that of the other biomarker. The Emotional temperature is calculated to track the emergence of compensatory skills in speech.

Conclusions. A biomarker based on the frame-view of speech signal has been implemented. The implementation has used the state of art feature set and thus is an unproved version of the classical Emotional Temperature. The biomarker has been used to automatically assess the recovery of two patients diagnosed with FAS. The biomarker has been compared against the global view biomarker and has advantages over it. It also has been compared to human evaluations and captures the same dynamics.

Place, publisher, year, edition, pages
2017. , 51 p.
Keyword [en]
Emotion Recognition, Pattern Recognition, Predictive Health Analytics, Cost-effective biomarker
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-15330OAI: oai:DiVA.org:bth-15330DiVA: diva2:1148929
Subject / course
DV2572 Master´s Thesis in Computer Science
Educational program
DVAXA Master of Science Programme in Computer Science
Supervisors
Examiners
Available from: 2017-10-16 Created: 2017-10-12 Last updated: 2017-10-16Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf