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Statistical voice activity detection based on integrated bispectrum likelihood ratio tests for robust speech
J Ramírez1, J M Górriz, J C Segura
1Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain. javierrp@ugr.es
The Journal of the Acoustical Society of America
|June 7, 2007
Summary
This study introduces a novel voice activity detector (VAD) using integrated bispectrum for improved speech processing in noisy environments. The new VAD demonstrates superior accuracy and performance compared to existing standards.
Area of Science:
- Signal Processing
- Speech Technology
- Statistical Signal Analysis
Background:
- Modern speech processing systems face challenges in extremely noisy conditions.
- Effective noise reduction and precise voice activity detection (VAD) are crucial for these systems.
Purpose of the Study:
- To develop a robust voice activity detector (VAD) overcoming technological barriers in noisy environments.
- To enhance speech processing system performance through improved speech/nonspeech detection.
Main Methods:
- Formulated statistical likelihood ratio tests using the integrated bispectrum of noisy signals.
- Defined integrated bispectrum as a cross-spectrum between the signal and its square.
- Incorporated contextual information into the decision rule for enhanced robustness.
Main Results:
- The integrated bispectrum offers computational savings and a stable variance estimator.
- The proposed VAD showed sustained advantages in speech/nonspeech detection accuracy.
- Demonstrated improved speech recognition performance compared to G.729, AMR, AFE, and other recent algorithms.
Conclusions:
- The integrated bispectrum-based VAD provides a significant advancement for speech processing in challenging acoustic conditions.
- This approach offers a computationally efficient and accurate solution for robust voice activity detection.
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