Single-sided speech quality measurement

dc.AffiliationOctober University for modern sciences and Arts (MSA)
dc.contributor.authorChan, Wai-Yip
dc.contributor.authorFalk, Tiago
dc.contributor.authorEl-Hennawey, Mohamed
dc.date.accessioned2020-02-05T10:24:48Z
dc.date.available2020-02-05T10:24:48Z
dc.date.issued2007
dc.descriptionMSA Google Scholaren_US
dc.description.abstractA non-intrusive speech quality estimation technique is based on statistical or probability models such as Gaussian Mixture Models (“GMMs”). Perceptual features are extracted from the received speech signal and assessed by an artificial reference model formed using statistical models. The models characterize the statistical behavior of speech features. Consistency measures between the input speech features and the models are calculated to form indicators of speech quality. The consistency values are mapped to a speech quality score using a mapping optimized using machine learning algorithms, such as Multivariate Adaptive Regression Splines (“MARS”). The technique provides competitive or better quality estimates relative to known techniques while having lower computational complexity.en_US
dc.description.sponsorshipAvaya Incen_US
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dc.identifier.urihttps://t.ly/y5Jn5
dc.language.isoenen_US
dc.publisherAvaya Incen_US
dc.subjectUniversity of Single-sided speech qualityen_US
dc.titleSingle-sided speech quality measurementen_US
dc.typeOtheren_US

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