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Automatic Speech Quality Assessment in Unified Communication: A Case Study
Linköping University, Department of Computer and Information Science, Software and Systems.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Automatisk utvärdering av samtalskvalitet inom integrerad kommunikation : en fallstudie (Swedish)
Abstract [en]

Speech as a medium for communication has always been important in its ability to convey our ideas, personality and emotions. It is therefore not strange that Quality of Experience (QoE) becomes central to any business relying on voice communication. Using Unified Communication (UC) systems, users can communicate with each other in several ways using many different devices, making QoE an important aspect for such systems. For this thesis, automatic methods for assessing speech quality of the voice calls in Briteback’s UC application is studied, including a comparison of the researched methods. Three methods all using a Gaussian Mixture Model (GMM) as a regressor, paired with extraction of Human Factor Cepstral Coefficients (HFCC), Gammatone Frequency Cepstral Coefficients (GFCC) and Modified Mel Frequency Cepstrum Coefficients (MMFCC) features respectively is studied. The method based on HFCC feature extraction shows better performance in general compared to the two other methods, but all methods show comparatively low performance compared to literature. This most likely stems from implementation errors, showing the difference between theory and practice in the literature, together with the lack of reference implementations. Further work with practical aspects in mind, such as reference implementations or verification tools can make the field more popular and increase its use in the real world.

Place, publisher, year, edition, pages
2019. , p. 45
Keywords [en]
speech, voice, communication, qoe, quality of experience, unified communication, uc, speech quality assessment, speech quality, voice calls, gaussian mixture model, gmm, gaussian mixture regression, gmr, mel frequency cepstrum coefficients, mfcc, human feature cepstrum coefficients, hfcc, gammatone frequency cepstral coefficients, gfcc
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:liu:diva-159794ISRN: LIU-IDA/LITH-EX-A--19/049--SEOAI: oai:DiVA.org:liu-159794DiVA, id: diva2:1344729
External cooperation
Briteback AB
Subject / course
Computer Engineering
Presentation
2019-06-12, Allen Newell, Linköpings universitet, Linköping, 15:00 (English)
Supervisors
Examiners
Available from: 2019-08-30 Created: 2019-08-21 Last updated: 2019-08-30Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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