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dc.contributor.authorJaiswal, Rahul Kumar
dc.contributor.authorDubey, Rajesh Kumar
dc.identifier.citationJaiswal, R. K. & Dubey, R. K. (2022). Non-intrusive speech quality assessment using context-aware neural networks. International Journal of Speech Technology, 25, 947–965.en_US
dc.description.abstractTo meet the human perceived quality of experience (QoE) while communicating over various Voice over Internet protocol (VoIP) applications, for example Google Meet, Microsoft Skype, Apple FaceTime, etc. a precise speech quality assessment metric is needed. The metric should be able to detect and segregate different types of noise degradations present in the surroundings before measuring and monitoring the quality of speech in real-time. Our research is motivated by the lack of clear evidence presenting speech quality metric that can firstly distinguish different types of noise degradations before providing speech quality prediction decision. To that end, this paper presents a novel non-intrusive speech quality assessment metric using context-aware neural networks in which the noise class (context) of the degraded or noisy speech signal is first identified using a classifier then deep neutral networks (DNNs) based speech quality metrics (SQMs) are trained and optimized for each noise class to obtain the noise class-specific (context-specific) optimized speech quality predictions (MOS scores). The noisy speech signals, that is, clean speech signals degraded by different types of background noises are taken from the NOIZEUS speech corpus. Results demonstrate that even in the presence of less number of speech samples available from the NOIZEUS speech corpus, the proposed metric outperforms in different contexts compared to the metric where the contexts are not classified before speech quality prediction.en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleNon-intrusive speech quality assessment using context-aware neural networksen_US
dc.title.alternativeNon-intrusive speech quality assessment using context-aware neural networksen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.rights.holder© 2022 The Author(s)en_US
dc.subject.nsiVDP::Teknologi: 500en_US
dc.source.journalInternational Journal of Speech Technologyen_US

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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal