SPQER: Speech Quality Evaluation Using Word Recognition for VoIP Communication in Lossy and Mobile Networks

DC FieldValueLanguage
dc.contributor.authorSchuetz, Bertram
dc.contributor.authorAschenbruck, Nils
dc.date.accessioned2021-12-23T15:59:11Z-
dc.date.available2021-12-23T15:59:11Z-
dc.date.issued2020
dc.identifier.urihttps://osnascholar.ub.uni-osnabrueck.de/handle/unios/3785-
dc.description.abstractIn this paper, we introduce SPQER (pronounced speaker), a novel approach to evaluate the quality of experience for real-time Voice over IP (VoIP) communication in mobile and lossy networks. Traditional speech quality metrics, e.g., Perceptual Evaluation of Speech Quality (PESQ) or the Hearing-Aid Speech Quality Index (HASQI), directly compare frequencies and amplitudes to calculate the received signal distortions. SPQER instead uses machine learning classification to evaluate the percentage of recognizable words in conjunction with a time-based decay function to penalize delay and cross-talking. So instead of evaluating noise, SPQER directly answers the question: What percentage of words is the recipient able to understand? We presented a sensitivity analysis, which is based on testbed experiments for different packet loss rates and simulated delays, to asses the impact of challenging link conditions. A final correlation analysis to a short user study shows that SPQER can better evaluate the amount of understandable words than PESQ and HASQI, while still giving a more precise indication about the voice quality than the Word Error Rate (WER) metric.
dc.language.isoen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartofIEEE OPEN JOURNAL OF THE COMPUTER SOCIETY
dc.subjectComputer Science
dc.subjectComputer Science, Hardware & Architecture
dc.subjectComputer Science, Information Systems
dc.subjectComputer Science, Interdisciplinary Applications
dc.subjectComputer Science, Theory & Methods
dc.subjectEngineering
dc.subjectEngineering, Electrical & Electronic
dc.subjectLossy networks
dc.subjectmachine learning
dc.subjectquality of service
dc.subjectvoice over IP
dc.titleSPQER: Speech Quality Evaluation Using Word Recognition for VoIP Communication in Lossy and Mobile Networks
dc.typejournal article
dc.identifier.doi10.1109/OJCS.2020.3011392
dc.identifier.isiISI:000723374500012
dc.description.volume1
dc.description.startpage145
dc.description.endpage154
dc.identifier.eissn26441268
dc.publisher.place445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
dcterms.isPartOf.abbreviationIEEE Open J. Comput. Soc.
dcterms.oaStatusBronze
crisitem.author.orcid0000-0002-5861-8896-
crisitem.author.netidAsNi712-
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