Speech Activity Detection Using a Fusion of Dense Convolutional Network in the Movie Audio

Speech activity detection (SAD) is a critical preparation process for speech-based applications. The speech activity detection is used to identify the speech in an audio recording. This paper aims to propose a speech activity detection on the entertainment media domain based on CNN. The fusion of two Dense Convolutional Network (DenseNet) with different feature extraction by using Dempster-Shafer theory (DS theory) was used to classify the speech segment. We combined acoustic features, which are the logmel spectrogram (LM), mel frequency cepstral coefficient (MFCC), chroma, spectral contrast, and tonnetz as the input feature. The combination of acoustic features operates on the raw speech signal and delivers it into a convolution neural network for classifying the speech. The result in this work shows that the proposed speech activity detection can achieve better performance (+1% Accuracy, +8% Precision, and +5% F1 score) than previous work in a more complicated noise environment.