Accent in speech samples: Support vector machines for classification and rule extraction

Pedersen, Carol and Diederich, Joachim (2007). Accent in speech samples: Support vector machines for classification and rule extraction. In J. Diederich (Ed.), Rule Extraction from Support Vector Machines (pp. 205-226) Berlin, Heidelberg, Germany: Springer-Verlag.


Author Pedersen, Carol
Diederich, Joachim
Title of chapter Accent in speech samples: Support vector machines for classification and rule extraction
Title of book Rule Extraction from Support Vector Machines
Place of Publication Berlin, Heidelberg, Germany
Publisher Springer-Verlag
Publication Year 2007
Sub-type Research book chapter (original research)
DOI 10.1007/978-3-540-75390-2_9
Series Studies in Computational Intelligence
ISBN 978-3-540-75389-6
ISSN 1860-949X
Editor J. Diederich
Volume number 80
Chapter number 9
Start page 205
End page 226
Total pages 22
Total chapters 10
Collection year 2008
Language eng
Subjects 280206 Speech Recognition
780101 Mathematical sciences
B1
Abstract/Summary Accent is the pattern of pronunciation which can identify a person’s linguistic, social or cultural background. It is an important source of inter-speaker variability and a particular problem for automated speech recognition. This study aims to investigate the effectiveness of rule extraction from support vector machines for speech accent classification. The presence of a speaker’s accent in the speech signal has significant implications for the accuracy of speech recognition because the effectiveness of an Automatic Speech Recognition System (ASR) is greatly reduced when the particular accent or dialect in the speech samples on which it is trained differs from the accent or dialect of the end-user [4] [14]. The correct identification of a speaker’s accent, and the subsequent use of the appropriately trained system, can be used to improve the efficiency and accuracy of the ASR application. If used in automated telephone helplines, analysing accent and then directing callers to the appropriately-accented response system may improve customer comfort and understanding. The increasing use of speech recognition technology in modern applications by people with a wide variety of linguistic and cultural backgrounds, means that addressing accent-related variability in speech is an important area of ongoing research. Rule extraction in this context can aid in the refinement of the design of a successful classifier, by discovering the contribution of the various input features, as well as by facilitating the comparison of the results with other machine learning methods.
Q-Index Code B1
Q-Index Status Confirmed Code

 
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