  The LEAP submission for DIHARD-III challenge is described in this paper. The
proposed system is composed of a speech bandwidth classifier, and diarization
systems fine-tuned for narrowband and wideband speech separately. We use an
end-to-end speaker diarization system for the narrowband conversational
telephone speech recordings. For the wideband multi-speaker recordings, we use
a neural embedding based clustering approach, similar to the baseline system.
The embeddings are extracted from a time-delay neural network (called
x-vectors) followed by the graph based path integral clustering (PIC) approach.
The LEAP system showed 24% and 18% relative improvements for Track-1 and
Track-2 respectively over the baseline system provided by the organizers. This
paper describes the challenge submission, the post-evaluation analysis and
improvements observed on the DIHARD-III dataset.
