Long-Term Spectral Pseudo-Entropy (LTSPE) Feature

Speech detection systems are known as a type of audio classifier systems which are used to recognize, detect or mark parts of audio signal including human speech. Here, a novel robust feature named Long-Term Spectral Pseudo-Entropy (LTSPE) is proposed to detect speech and its purpose is to improve performance in combination with other features, increase accuracy and to have acceptable performance. Experimental results show that if LTSPE is combined with other features, performance of the detector is improved. Moreover, this feature has higher accuracy compared to similar ones.

语音检测系统，亦称音频分类系统，旨在识别、检测或标记音频信号中包括人声的部分。本研究提出一种新颖的鲁棒性特征——长期光谱伪熵（Long-Term Spectral Pseudo-Entropy，简称LTSPE），旨在检测语音。其目的是在与其他特征结合使用时提升性能，提高准确率，并保持可接受的性能。实验结果表明，当LTSPE与其他特征结合时，检测器的性能得到提升。此外，相较于其他类似特征，该特征具有更高的准确率。