Endpoint Detection Using Short-Term Energy and Spectral Entropy

In speech signal processing, endpoint detection serves as a fundamental yet critical step for identifying the start and end points of speech segments, thereby eliminating irrelevant silence or noise portions. Short-term energy and spectral entropy represent two widely adopted features that effectively distinguish speech segments from non-speech intervals.

Short-term energy captures intensity variations within brief time windows. Typically, speech segments exhibit significantly higher energy levels compared to silent or background noise intervals. By implementing appropriate energy thresholds (e.g., using numpy's sliding window functions), preliminary voice boundaries can be determined. However, relying solely on energy-based detection may underperform in high-noise environments where strong interference can produce elevated energy readings.

Spectral entropy quantifies signal "disorder" through frequency distribution analysis. Speech signals generally demonstrate lower spectral entropy due to concentrated energy in specific bands (e.g., formant regions), while noise exhibits higher entropy with more uniform spectral distribution. Combining spectral entropy calculations (implementable via FFT and Shannon entropy formulas) compensates for energy-based limitations, enhancing detection robustness.

A basic endpoint detection algorithm integrates both features through these computational steps: First, compute frame-wise short-term energy using overlapping window functions (e.g., Hamming window) and apply dynamic/static thresholding to identify high-energy segments. Next, calculate spectral entropy by normalizing power spectrum and applying entropy computation to validate candidate segments' spectral characteristics. Finally, employ logical operations (e.g., dual conditions for energy and entropy thresholds) to determine final speech endpoints.

Future optimizations may incorporate adaptive thresholding algorithms, multi-feature fusion (combining zero-crossing rate, MFCCs), and machine learning approaches (e.g., SVM or neural networks) to improve detection accuracy under varying acoustic conditions.