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Full-Text Articles in Computer Engineering
Robustness Of Speech Quality Metrics To Background Noise And Network Degradations: Comparing Visqol, Pesq And Polqa, Andrew Hines, Naomi Harte, Jan Skoglund, Anil Kokaram
Robustness Of Speech Quality Metrics To Background Noise And Network Degradations: Comparing Visqol, Pesq And Polqa, Andrew Hines, Naomi Harte, Jan Skoglund, Anil Kokaram
Conference papers
The Virtual Speech Quality Objective Listener (ViSQOL) is a new objective speech quality model. It is a signal based full reference metric that uses a spectro-temporal measure of similarity between a reference and a test speech signal. ViSQOL aims to predict the overall quality of experience for the end listener whether the cause of speech quality degradation is due to ambient noise, or transmission channel degradations. This paper describes the algorithm and tests the model using two speech corpora: NOIZEUS and E4. The NOIZEUS corpus contains speech under a variety of background noise types, speech enhancement methods, and SNR levels. …
Visqol: The Virtual Speech Quality Objective Listener, Andrew Hines, Jan Skoglund, Anil Kokaram, Naomi Harte
Visqol: The Virtual Speech Quality Objective Listener, Andrew Hines, Jan Skoglund, Anil Kokaram, Naomi Harte
Conference papers
A model of human speech quality perception has been developed to provide an objective measure for predicting subjective quality assessments. The Virtual Speech Quality Objective Listener (ViSQOL) model is a signal based full reference metric that uses a spectro-temporal measure of similarity between a reference and a test speech signal. This paper describes the algorithm and compares the results with PESQ for common problems in VoIP: clock drift, associated time warping and jitter. The results indicate that ViSQOL is less prone to underestimation of speech quality in both scenarios than the ITU standard.
Comparing Hearing Aid Algorithm Performance Using Simulated Performance Intensity Functions, Andrew Hines, Naomi Harte
Comparing Hearing Aid Algorithm Performance Using Simulated Performance Intensity Functions, Andrew Hines, Naomi Harte
Conference papers
Simulated performance intensity functions were used to quantitatively discriminate speech intelligibility through phoneme discrimination assessment. Listener test results for subjects with a wide range of sensorineural hearing losses were simulated using an auditory nerve model and compared to real listeners' unaided and aided performance. Simulations of NAL-RP and DSL 4.0 fitting algorithms were compared. Auditory nerve discharge patterns from the model were presented as neurograms. An automated ranking process was used to quantify neurogram degradation using a new measure, the Neurogram Similarity Index Measure (NSIM). The measure has previously been shown to correlate well in predictions of phoneme discrimination for …