Visualization of Voice Disorders Using the Sammon Transform

The Sammon Transform performs data projections in a topology-preserving manner on the basis of an arbitrary distance measure. We use the weights of the observation probabilities of semi-continuous HMMs that were adapted to the current speaker as input. Experiments on laryngectomized speakers with tr...

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Bibliographic Details
Published inLecture notes in computer science pp. 589 - 596
Main Authors Haderlein, Tino, Zorn, Dominik, Steidl, Stefan, Nöth, Elmar, Shozakai, Makoto, Schuster, Maria
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2006
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Exact sciences and technology
Speech and sound recognition and synthesis. Linguistics
Speech Effort
Speech Recognition
Speech Recognition System
Speech Recognizer
Voice Disorder
Visualization
Probabilistic approach
Linguistics
Hidden Markov model
Natural language
Markov model
Topology
Voice
Speaker
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Summary:The Sammon Transform performs data projections in a topology-preserving manner on the basis of an arbitrary distance measure. We use the weights of the observation probabilities of semi-continuous HMMs that were adapted to the current speaker as input. Experiments on laryngectomized speakers with tracheoesophageal substitute voice, hoarse, and normal speakers show encouraging results. Different speaker groups are separated in 2-D space, and the projection of a new speaker into the Sammon map allows prediction of his or her kind of voice pathology. The method can thus be used as an objective, automated support for the evaluation of voice disorders, and it visualizes them in a way that is convenient for speech therapists.
ISBN:9783540390909
3540390901
ISSN:0302-9743
1611-3349
DOI:10.1007/11846406_74