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Voice Activity Detection (VAD) is a fundamental preprocessing step inautomatic speech recognition. This is especially true within the broadcastindustry where a wide variety of audio materials and recording conditions areencountered. Based on previous studies which indicate that xvector embeddingscan be applied to a diverse set of audio classification tasks, we investigatethe suitability of x-vectors in discriminating speech from noise. We find thatthe proposed x-vector based VAD system achieves the best reported score indetecting clean speech on AVA-Speech, whilst retaining robust VAD performancein the presence of noise and music. Furthermore, we integrate the x-vectorbased VAD system into an existing STT pipeline and compare its performance onmultiple broadcast datasets against a baseline system with WebRTC VAD.Crucially, our proposed x-vector based VAD improves the accuracy of STTtranscription on real-world broadcast audio

X-Vector based voice activity detection for multi-genre broadcast speech-to-text