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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 4.75 kbps.
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Figure 1: Error for each speech feature when the audio signal is compressed using AMR-NB codec at 4.75 kbps.

Mentions: Anticipating the effects compression has on speech metrics is arduous. Figure 1 gives the power spectrum density (PSD) of an adult male speaker uttering a vowel. This Figure shows the PSD of the original signal, and after being compressed by the lowest possible bit-rate of the AMR codec (4.75 kbps). The difference between the two spectra is also given. Clearly the difference is large near the maximum limit of the frequency spectrum (>3000 Hz), which comprises the fine grain structure of the signal. However, there are differences across the spectrum likely caused by the codec encoding the signal using fewer bits than the original representation.


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 4.75 kbps.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 1: Error for each speech feature when the audio signal is compressed using AMR-NB codec at 4.75 kbps.
Mentions: Anticipating the effects compression has on speech metrics is arduous. Figure 1 gives the power spectrum density (PSD) of an adult male speaker uttering a vowel. This Figure shows the PSD of the original signal, and after being compressed by the lowest possible bit-rate of the AMR codec (4.75 kbps). The difference between the two spectra is also given. Clearly the difference is large near the maximum limit of the frequency spectrum (>3000 Hz), which comprises the fine grain structure of the signal. However, there are differences across the spectrum likely caused by the codec encoding the signal using fewer bits than the original representation.

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.