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ERIC Number: ED564514
Record Type: Non-Journal
Publication Date: 2013
Pages: 123
Abstractor: As Provided
Reference Count: N/A
ISBN: 978-1-3036-0662-5
Shibboleth: An Automated Foreign Accent Identification Program
Frost, Wende
ProQuest LLC, Ph.D. Dissertation, Arizona State University
The speech of non-native (L2) speakers of a language contains phonological rules that differentiate them from native speakers. These phonological rules characterize or distinguish accents in an L2. The Shibboleth program creates combinatorial rule-sets to describe the phonological pattern of these accents and classifies L2 speakers into their native language. The training and classification is done in Shibboleth by support vector machines using a Gaussian radial basis kernel. In one experiment run using Shibboleth, the program correctly identified the native language (L1) of a speaker of unknown origin 42% of the time when there were six possible L1s in which to classify the speaker. This rate is significantly better than the 17% chance classification rate. Chi-squared test (1, N = 24) =10.800, p = 0.0010 In a second experiment, Shibboleth was not able to determine the native language family of a speaker of unknown origin at a rate better than chance (33-44%) when the L1 was not in the transcripts used for training the language family rule-set. Chi-squared test (1, N = 18) =1.000, p = 0.3173 The 318 participants for both experiments were from the Speech Accent Archive (Weinberger, 2013), and ranged in age from 17 to 80 years old. Forty percent of the speakers were female and 60% were male. The factor that most influenced correct classification was higher age of onset for the L2. A higher number of years spent living in an English-speaking country did not have the expected positive effect on classification. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page:]
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Publication Type: Dissertations/Theses - Doctoral Dissertations
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A