0000000000274234

AUTHOR

Andrew Hines

0000-0001-9636-2556

showing 1 related works from this author

Towards a Non-Intrusive Context-Aware Speech Quality Model

2020

Understanding how humans judge perceived speech quality while interacting through Voice over Internet Protocol (VoIP) applications in real-time is essential to build a robust and accurate speech quality prediction model. Speech quality is degraded in the presence of background noise reducing the Quality of Experience (QoE). Speech Enhancement (SE) algorithms can improve speech quality in noisy environments. The publicly available NOIZEUS speech corpus contains speech in environmental background noise babble, car, street, and train at two Signal-to-noise ratio (SNRs) 5dB and 10dB. Objective Speech Quality Metrics (OSQM) are used to monitor and measure speech quality for VoIP applications. Th…

Context modelVoice activity detectionNoise measurementComputer scienceSpeech recognitionMean opinion score020206 networking & telecommunicationsSpeech corpus02 engineering and technology01 natural sciencesBackground noiseSpeech enhancement0103 physical sciences0202 electrical engineering electronic engineering information engineeringQuality of experience010301 acoustics2020 31st Irish Signals and Systems Conference (ISSC)
researchProduct