Swedish Dialect Classification using Artificial Neural Networks and Guassian Mixture Models

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dc.contributor.authorBlomqvist, Viktor
dc.contributor.authorLidberg, David
dc.contributor.departmentChalmers tekniska högskola / Institutionen för matematiska vetenskapersv
dc.contributor.departmentChalmers University of Technology / Department of Mathematical Sciencesen
dc.date.accessioned2019-07-03T14:37:47Z
dc.date.available2019-07-03T14:37:47Z
dc.date.issued2017
dc.description.abstractVariations due to speaker dialects are one of the main problems in automatic speech recognition. A possible solution to this issue is to have a separate classifier identify the dialect of a speaker and then load an appropriate speech recognition system. This thesis investigates classification of seven Swedish dialects based on the SweDia2000 database. Classification was done using Gaussian mixture models, which are a widely used technique in speech processing. Inspired by recent progress in deep learning techniques for speech recognition, convolutional neural networks and multi-layered perceptrons were also implemented. Data was preprocessed using both mel-frequency coefficients, and a novel feature extraction technique using path signatures. Results showed high variance in classification accuracy during cross validations even for simple models, suggesting a limitation in the amount of available data for the classification problems formulated in this project. The Gaussian mixture models reached the highest accuracy of 61.3% on test set, based on singe-word classification. Performance is greatly improved by including multiple words, achieving around 80% classification accuracy using 12 words.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/251852
dc.language.isoeng
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectGrundläggande vetenskaper
dc.subjectMatematik
dc.subjectBasic Sciences
dc.subjectMathematics
dc.titleSwedish Dialect Classification using Artificial Neural Networks and Guassian Mixture Models
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeApplied physics (MPAPP), MSc
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