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Adaptive Multi-Rate Compression Effects on Vowel Analysis.

Ireland D, Knuepffer C, McBride SJ - Front Bioeng Biotechnol (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Computational Informatics, Australian e-Health Research Centre, CSIRO , Brisbane, QLD , Australia.

ABSTRACT
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.

No MeSH data available.


f0 and formant errors for each spoken vowel when the audio signal is compressed using AMR-WB codec at 23.85 kbps.
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Figure 9: f0 and formant errors for each spoken vowel when the audio signal is compressed using AMR-WB codec at 23.85 kbps.


Adaptive Multi-Rate Compression Effects on Vowel Analysis.

Ireland D, Knuepffer C, McBride SJ - Front Bioeng Biotechnol (2015)

f0 and formant errors for each spoken vowel when the audio signal is compressed using AMR-WB codec at 23.85 kbps.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4542648&req=5

Figure 9: f0 and formant errors for each spoken vowel when the audio signal is compressed using AMR-WB codec at 23.85 kbps.
Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Computational Informatics, Australian e-Health Research Centre, CSIRO , Brisbane, QLD , Australia.

ABSTRACT
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.

No MeSH data available.