Environment aware speaker diarization for moving targets using parallel DNN-based recognizers

Abstract

Current diarization algorithms are commonly applied to the outputs of single non-moving microphones. They do not explicitly identify the content of overlapped segments from multiple speakers or acoustic events. This paper presents an acoustic environment aware child-adult diarization applied to the audio recorded by a single microphone attached to moving targets under realistic high noise conditions. The proposed system exploits a parallel deep neural network and hidden Markov model based approach which enables tracking of rapid turn changes in audio segments as well as capturing the cross talk labels for overlapped speech. It outperforms the state-of-the-art diarization systems without the need to prior clustering or front-end speech activity detection.