End-to-End Diarization for Variable Number of Speakers with Local-Global Networks and Discriminative Speaker Embeddings

• URL: http://arxiv.org/abs/2105.02096v1
• Date: Wed, 5 May 2021 14:55:29 GMT
• Title: End-to-End Diarization for Variable Number of Speakers with Local-Global Networks and Discriminative Speaker Embeddings
• Authors: Soumi Maiti, Hakan Erdogan, Kevin Wilson, Scott Wisdom, Shinji Watanabe and John R. Hershey
• Abstract summary: We present an end-to-end deep network model that performs meeting diarization from single-channel audio recordings. The proposed system is designed to handle meetings with unknown numbers of speakers, using variable-number permutation-invariant cross-entropy based loss functions.
• Score: 66.50782702086575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present an end-to-end deep network model that performs meeting diarization from single-channel audio recordings. End-to-end diarization models have the advantage of handling speaker overlap and enabling straightforward handling of discriminative training, unlike traditional clustering-based diarization methods. The proposed system is designed to handle meetings with unknown numbers of speakers, using variable-number permutation-invariant cross-entropy based loss functions. We introduce several components that appear to help with diarization performance, including a local convolutional network followed by a global self-attention module, multi-task transfer learning using a speaker identification component, and a sequential approach where the model is refined with a second stage. These are trained and validated on simulated meeting data based on LibriSpeech and LibriTTS datasets; final evaluations are done using LibriCSS, which consists of simulated meetings recorded using real acoustics via loudspeaker playback. The proposed model performs better than previously proposed end-to-end diarization models on these data.

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