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A subspace approach based on embedded prewhitening for voice activity detection
Dong Kook Kim1, Joon-Hyuk Chang
1School of Electronic and Computer Engineering, Chonnam National University, Yongbong, 500-757 Gwangju, Republic of Korea. dkim@chonnam.ac.kr
The Journal of the Acoustical Society of America
|November 18, 2011
Summary
This study introduces a novel subspace approach for voice activity detection (VAD). This method enhances performance, especially in noisy environments, outperforming traditional Gaussian models.
Area of Science:
- Signal Processing
- Speech Recognition
- Acoustics
Background:
- Voice Activity Detection (VAD) is crucial for speech processing systems.
- Traditional VAD methods can struggle in low signal-to-noise ratio (SNR) conditions.
- Subspace methods offer potential for improved VAD performance.
Purpose of the Study:
- To propose a new subspace approach for Voice Activity Detection (VAD).
- To evaluate the performance of the proposed VAD algorithm against existing methods.
- To demonstrate the effectiveness of the approach in challenging acoustic environments.
Main Methods:
- A subspace approach utilizing an embedded prewhitening scheme.
- Simultaneous diagonalization of clean speech and noise covariance matrices.
- A decision rule based on the likelihood ratio test in the signal subspace domain.
Main Results:
- The proposed subspace-based VAD algorithm demonstrates superior performance.
- Outperforms conventional Gaussian model-based methods in low SNR conditions.
- Effective in distinguishing speech from noise in challenging environments.
Conclusions:
- The subspace approach offers a robust solution for VAD.
- The embedded prewhitening scheme enhances VAD accuracy.
- This method is particularly beneficial for applications with low signal quality.
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