  Voice activity detection is an essential pre-processing component for
speech-related tasks such as automatic speech recognition (ASR). Traditional
supervised VAD systems obtain frame-level labels from an ASR pipeline by using,
e.g., a Hidden Markov model. These ASR models are commonly trained on clean and
fully transcribed data, limiting VAD systems to be trained on clean or
synthetically noised datasets. Therefore, a major challenge for supervised VAD
systems is their generalization towards noisy, real-world data. This work
proposes a data-driven teacher-student approach for VAD, which utilizes vast
and unconstrained audio data for training. Unlike previous approaches, only
weak labels during teacher training are required, enabling the utilization of
any real-world, potentially noisy dataset. Our approach firstly trains a
teacher model on a source dataset (Audioset) using clip-level supervision.
After training, the teacher provides frame-level guidance to a student model on
an unlabeled, target dataset. A multitude of student models trained on mid- to
large-sized datasets are investigated (Audioset, Voxceleb, NIST SRE). Our
approach is then respectively evaluated on clean, artificially noised, and
real-world data. We observe significant performance gains in artificially
noised and real-world scenarios. Lastly, we compare our approach against other
unsupervised and supervised VAD methods, demonstrating our method's
superiority.
