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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.


Error for each speech feature when the audio signal is compressed using AMR-NB codec at 12.20 kbps.
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Figure 4: Error for each speech feature when the audio signal is compressed using AMR-NB codec at 12.20 kbps.

Mentions: Table 2 shows the mean and SD of the resultant error when the audio is compressed using AMR-NB codec at all possible bit-rates. The complete data for bit-rates 4.75 kbps, 7.95 kbps, and 12.2 kbps are given in box-and-whisker form in Figures 2–4, respectively. The box-and-whisker plot was chosen because it readily displays key measures: the enclosed box depicts the lower quartile, median, and upper quartile while the arms extending from the box (whiskers) show the smallest and largest observation of the statistical data. Table elements in boldface represent the metrics that showed a high significance (p-value < αc).


Adaptive Multi-Rate Compression Effects on Vowel Analysis.

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

Error for each speech feature when the audio signal is compressed using AMR-NB codec at 12.20 kbps.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 4: Error for each speech feature when the audio signal is compressed using AMR-NB codec at 12.20 kbps.
Mentions: Table 2 shows the mean and SD of the resultant error when the audio is compressed using AMR-NB codec at all possible bit-rates. The complete data for bit-rates 4.75 kbps, 7.95 kbps, and 12.2 kbps are given in box-and-whisker form in Figures 2–4, respectively. The box-and-whisker plot was chosen because it readily displays key measures: the enclosed box depicts the lower quartile, median, and upper quartile while the arms extending from the box (whiskers) show the smallest and largest observation of the statistical data. Table elements in boldface represent the metrics that showed a high significance (p-value < αc).

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.