X-Vector based voice activity detection for multi-genre broadcast speech-to-text
This addresses VAD for the broadcast industry, offering incremental improvements in handling diverse audio conditions.
The paper tackled voice activity detection (VAD) for broadcast speech-to-text by proposing an x-vector based system, which achieved the best reported score on AVA-Speech for clean speech and improved transcription accuracy on real-world broadcast audio compared to a baseline.
Voice Activity Detection (VAD) is a fundamental preprocessing step in automatic speech recognition. This is especially true within the broadcast industry where a wide variety of audio materials and recording conditions are encountered. Based on previous studies which indicate that xvector embeddings can be applied to a diverse set of audio classification tasks, we investigate the suitability of x-vectors in discriminating speech from noise. We find that the proposed x-vector based VAD system achieves the best reported score in detecting clean speech on AVA-Speech, whilst retaining robust VAD performance in the presence of noise and music. Furthermore, we integrate the x-vector based VAD system into an existing STT pipeline and compare its performance on multiple broadcast datasets against a baseline system with WebRTC VAD. Crucially, our proposed x-vector based VAD improves the accuracy of STT transcription on real-world broadcast audio