CORDIS Project
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This project focuses on improving Speaker Diarization, which identifies who spoke when in audio recordings. It aims to enhance Bayesian methods and integrate Deep Neural Networks for better performance in speaker recognition systems, benefiting industries reliant on speech data analysis.
The proposed project deals with Speaker Diarization (SD) which is commonly defined as the task of answering the question “who spoke when?” in a speech recording.
The first objective of the proposal is to optimize the Bayesian approach to SD, which has shown to be promising for the tasks.
For Variational Bayes (VB) inference, that is very sensitive to initialization, we will develop new fast ways of obtaining a good starting point.
We will also explore alternative inference methods, such as colla…
VYSOKE UCENI TECHNICKE V BRNE
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