Title: Triplet Network with Attention for Speaker Diarization,
Abstract: In automatic speech processing systems, speaker diarization is a crucial
front-end component to separate segments from different speakers. Inspired by
the recent success of deep neural networks (DNNs) in semantic inferencing,
triplet loss-based architectures have been successfully used for this problem.
However, existing work utilizes conventional i-vectors as the input
representation and builds simple fully connected networks for metric learning,
thus not fully leveraging the modeling power of DNN architectures. This paper
investigates the importance of learning effective representations from the
sequences directly in metric learning pipelines for speaker diarization. More
specifically, we propose to employ attention models to learn embeddings and the
metric jointly in an end-to-end fashion. Experiments are conducted on the
CALLHOME conversational speech corpus. The diarization results demonstrate
that, besides providing a unified model, the proposed approach achieves
improved performance when compared against existing approaches.