Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
Language Classification Using Neural Networks
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

In this project a model has been created that with an audio sequence as input can classify the language being spoken to be either english or french. The focus of the project has been to experiment with different ways to process audio files and to design a neural network in order to maximize the performance for the task of language classification. The purpose of the project was to investigate the highest reachable accuracy and to examine what sample length that would be appropriate in order to be useful in voice control application. The signal processing part dealt mainly with how enveloping, Mel frequency ceptral coefficients (MFCC) and Mel frequency spectral coefficients (MFSC) could be used to enhance the accuracy of the model. The neural network design focused on how the width and depth of the network and the use of dropouts could be used to increase the performance. The experiments resulted in a model with a maximum accuracy of 92,30 % that could outperform humans for samples of approximately 1,2 seconds of shorter. A suitable sample length to be usable in other applications was concluded to be in the interval of 0,7 to 1,5 seconds.

Place, publisher, year, edition, pages
2019. , p. 38
Series
TVE-F ; 19016
National Category
Other Engineering and Technologies not elsewhere specified
Identifiers
URN: urn:nbn:se:uu:diva-385537OAI: oai:DiVA.org:uu-385537DiVA, id: diva2:1324945
Educational program
Master Programme in Engineering Physics
Supervisors
Examiners
Available from: 2019-06-25 Created: 2019-06-14 Last updated: 2019-06-25Bibliographically approved

Open Access in DiVA

fulltext(1902 kB)29 downloads
File information
File name FULLTEXT01.pdfFile size 1902 kBChecksum SHA-512
c85f6eb0bfde9dcf66c2d4ba8642991f9a4ae2da72d827e9d735e032b069e6113850e6064987934a4ecbfc5197c91fc469b9085e56be3b4c28047ac8ac90942c
Type fulltextMimetype application/pdf

Other Engineering and Technologies not elsewhere specified

Search outside of DiVA

GoogleGoogle Scholar
Total: 29 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 32 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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