Long-Term Spectral Pseudo-Entropy (LTSPE): A New Robust Feature for Speech Activity Detection

Speech detection systems are known as a type of audio classifier systems which are used to recognize, detect, or mark parts of an audio signal including human speech. Applications of these types of systems include speech enhancement, noise cancellation, identification, reducing the size of audio signals in communication and storage, and many other applications. 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. To this end, the proposed method is compared to other new and well-known methods of this context in two different conditions, with uses a speech enhancement algorithm to improve the quality of audio signals and without using speech enhancement. In this research, the MUSAN dataset is used, which includes a large number of audio signals in the form of music, speech, and noise. Also, various known methods of machine learning are used. As well as criteria for measuring accuracy and error in this paper are the criteria for F-Score and Equal-Error Rate (EER), respectively. Experimental results on MUSAN dataset show that if the proposed feature LTSPE is combined with other features, the performance of the detector is improved. Moreover, the proposed feature has higher accuracy and lower error compared to similar ones.