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End-To-End Diarization For Variable Number Of Speakers With Local-Global Networks And Discriminative Speaker Embeddings

Soumi Maiti, Hakan Erdogan, Kevin Wilson, Scott Wisdom, Shinji Watanabe, John R. Hershey

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    Length: 00:08:11
11 Jun 2021

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.

Chairs:
Man-Wai Mak

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