Speech Endpoint Detection Based on Short-Time Zero Entropy Method

In this article, we explore speech endpoint detection utilizing the short-time zero entropy method. This approach is widely employed in speech signal analysis, where the core principle involves calculating zero entropy within short-time windows and comparing entropy values across different temporal segments to identify signal endpoints. In speech processing applications, endpoint detection serves as a critical task frequently applied in speech recognition, speech synthesis, and related domains. The implementation typically involves framing the speech signal into overlapping windows (commonly 20-30ms duration) and computing the zero entropy for each frame using probability distribution analysis. Key algorithmic steps include: 1. Pre-emphasis filtering to enhance high-frequency components 2. Frame blocking with Hamming window application 3. Zero entropy calculation through probability density estimation 4. Threshold-based decision making for endpoint identification The short-time zero entropy method has demonstrated excellent performance in practical applications, exhibiting high accuracy and robustness against environmental noise. This article provides detailed explanations of the method's theoretical foundations and implementation procedures, including code snippets showcasing entropy calculation and threshold optimization techniques. We further analyze the method's advantages in computational efficiency and limitations in low Signal-to-Noise Ratio (SNR) scenarios. Through comprehensive technical discussion and code integration, readers will gain deeper understanding of short-time zero entropy based endpoint detection technology, enabling effective implementation in speech processing systems.