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This paper deals with the task of Speech Activity Detection (SAD). Our goal is to develop a SAD module suitable for a system for broadcast data transcription. Various Deep Neural Networks (DNNs) are evaluated for this purpose. Training of DNNs is performed using speech and non-speech data as well as artificial data created by mixing of both these data types at a desired level of Signal-to-Noise Ratio (SNR). The output from each DNN is smoothed using a decoder based on Weighted Finite State Transducers (WFSTs). The presented experimental results show that the use of the resulting SAD module leads to a) a slight improvement in transcription accuracy and b) a significant reduction in the computation time needed for transcription.

Study on the Use of Deep Neural Networks for Speech Activity Detection in Broadcast Recordings