Adaptive Multi-Rate Compression Effects on Vowel Analysis.
Signal processing on digitally sampled vowel sounds for the detection of pathological voices has been firmly established. This work examines compression artifacts on vowel speech samples that have been compressed using the adaptive multi-rate codec at various bit-rates. Whereas previous work has used the sensitivity of machine learning algorithm to test for accuracy, this work examines the changes in the extracted speech features themselves and thus report new findings on the usefulness of a particular feature. We believe this work will have potential impact for future research on remote monitoring as the identification and exclusion of an ill-defined speech feature that has been hitherto used, will ultimately increase the robustness of the system.
Computational Informatics, Australian e-Health Research Centre, CSIRO , Brisbane, QLD , Australia.
David
D
Ireland
Asia-Pacific Centre for Neuromodulation, UQ Centre for Clinical Research, University of Queensland , Brisbane, QLD , Australia ; School of Information Technology and Electrical Engineering, University of Queensland , Brisbane, QLD , Australia.
Christina
C
Knuepffer
Computational Informatics, Australian e-Health Research Centre, CSIRO , Brisbane, QLD , Australia.
Simon J
SJ
McBride
adaptive multi-rate codec; speech compression; speech processing; vowel sounds