Enrollment-less training for personalized voice activity detection

Understand

We present a novel personalized voice activity detection (PVAD) learning method that does not require enrollment data during training.

• PVAD is a task to detect the speech segments of a specific target speaker at the frame level using enrollment speech of the target speaker.
• Since PVAD must learn speakers' speech variations to clarify the boundary between speakers, studies on PVAD used large-scale datasets that contain many utterances for each speaker.
• However, the datasets to train a PVAD model are often limited because substantial cost is needed to prepare such a dataset.