ASR-Aware End-to-End Neural Diarization

Khare, Aparna; Han, Eun‐Jung; Yuguang, Yang,; Stolcke, Andreas

doi:10.1109/icassp43922.2022.9746964

Cited by 6 publications

(1 citation statement)

References 22 publications

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“…Multi-tasking reinforcing speaker recognition has also been explored in other works [Rybicka et al, 2022, Pan et al, 2022. EEND has also been aided by features extracted from an ASR model in [Khare et al, 2022] and in a unified framework to perform diarization and separation in [Maiti et al, 2023].…”

Section: Eend Extensionsmentioning

confidence: 99%

From Simulated Mixtures to Simulated Conversations as Training Data for End-to-End Neural Diarization

Landini¹,

Lozano-Díez²,

Díez³

et al. 2022

Interspeech 2022

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End-to-end diarization presents an attractive alternative to standard cascaded diarization systems because a single system can handle all aspects of the task at once. Many flavors of end-to-end models have been proposed but all of them require (so far non-existing) large amounts of annotated data for training. The compromise solution consists in generating synthetic data and the recently proposed simulated conversations (SC) have shown remarkable improvements over the original simulated mixtures (SM). In this work, we create SC with multiple speakers per conversation and show that they allow for substantially better performance than SM, also reducing the dependence on a fine-tuning stage. We also create SC with wide-band public audio sources and present an analysis on several evaluation sets. Together with this publication, we release the recipes for generating such data and models trained on public sets as well as the implementation to efficiently handle multiple speakers per conversation and an auxiliary voice activity detection loss.

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