Voice Activity Detection

Voice activity detection (VAD) aims to accurately identify speech segments within audio recordings, a crucial preprocessing step for numerous applications like speech recognition and speaker diarization. Current research emphasizes improving VAD robustness in challenging acoustic conditions (noise, reverberation, overlapping speech) using lightweight neural networks (e.g., convolutional, recurrent, and transformer architectures), often incorporating multi-channel processing and self-supervised learning techniques. These advancements are driving improvements in real-time applications, particularly in areas like hands-free communication, ecological monitoring, and personalized audio processing, where efficient and accurate speech detection is paramount.