一种基于语音图信号处理的端点检测方法

本文通过将语音信号处理与图信号处理相结合，为语音样点构建出一种基于遗忘因子的遗忘图拓扑结构，利用基于遗忘图拓扑结构的图邻接矩阵所定义的图傅里叶变换（Graph Fourier Transform， GFT），研究语音图信号的图频域特性。并在此研究基础上，本文将基于自适应子带谱熵（Adaptive Band-partitioning Spectral Entropy， ABSE）算法的端点检测方法拓展至图频域，设计了一种图自适应子带谱熵（Graph Adaptive Band-partitioning Spectral Entropy， GABSE）算法。实验表明，本文所提出的基于遗忘图的GABSE算法可以使得语音段与非语音段谱熵差异更加显著，较传统ABSE算法端点检测及rVAD语音端点检测方法正确率提高了10%~20%，同时也验证了此语音遗忘图结构有效性。 

An Endpoint Detection Method Based on Speech Graph Signal Processing

This paper proposes a forgotten graph topology for speech signals based on forgotten factor by applying graph technologies. Specifically， by using the Graph Fourier transform （GFT） based on the graph adjacency matrix of the forgotten graph topology， we can investigate the characteristics of speech signals in the graph frequency domain. Based on this research， we design a graph adaptive band-partitioning spectral entropy （GABSE） algorithm by extending the classical adaptive band-partitioning spectral entropy （ABSE） algorithm. Our experiments show that the performance of the proposed GABSE method outperforms that of traditional methods. More specifically， the accuracy of endpoint detection of the GABSE algorithm is 10%~20% higher than that of the traditional ABSE algorithm and the rVAD algorithm， which further verifies the effectiveness of the proposed forgotten graph for speech signals.