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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

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Adaptive Multi-Rate Compression Effects on Vowel Analysis.

David Ireland¹, Christina Knuepffer², Simon J McBride¹

¹Computational Informatics, Australian e-Health Research Centre, CSIRO , Brisbane, QLD , Australia.

Frontiers in Bioengineering and Biotechnology

|September 9, 2015

Summary

This study reveals how audio compression affects speech features used for pathological voice detection. Identifying and removing unreliable features improves remote voice monitoring systems.

Keywords:

adaptive multi-rate codec speech compression speech processing vowel sounds

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fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals

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Area of Science:

Digital Signal Processing
Speech Analysis
Biomedical Engineering

Background:

Pathological voice detection relies on signal processing of vowel sounds.
Previous research often uses machine learning accuracy to assess feature reliability.
Compression artifacts can impact the integrity of speech samples.

Purpose of the Study:

To investigate the effects of audio compression artifacts on extracted speech features.
To identify and evaluate the usefulness of specific speech features for pathological voice detection under compression.
To enhance the robustness of remote voice monitoring systems by excluding ill-defined features.

Main Methods:

Speech samples of vowels were compressed using the adaptive multi-rate (AMR) codec at various bit-rates.
Analysis focused on changes in extracted speech features rather than machine learning model accuracy.
Specific speech features were examined for their sensitivity to compression artifacts.

Main Results:

Compression artifacts introduce variations in certain speech features.
The study identified specific features that are unreliable for pathological voice detection after compression.
A particular speech feature's diminished usefulness under compression was highlighted.

Conclusions:

Understanding compression artifact impact is crucial for accurate pathological voice detection.
Excluding compression-sensitive, ill-defined speech features enhances system robustness.
This research supports the development of more reliable remote voice monitoring technologies.