arya24@interspeech_2024@ISCA

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#1 Exploiting Wavelet Scattering Transform for an Unsupervised Speaker Diarization in Deep Neural Network Framework [PDF] [Copy] [Kimi] [REL]

Authors: Arunav Arya, Murtiza Ali, Karan Nathwani

Advancements in diarization have prompted the development of supervised learning models. These models extract fixed-length embeddings from audio files of varying lengths. Despite challenges, commercial API models like Speechbrain, Resemblyzer, Whisper AI, and Pyannote have addressed this issue. However, these models typically utilize Mel-Frequency Cepstral Coefficients (MFCC) features, convolution layers, and dimension reduction techniques to create embeddings. Our proposal method introduces a Wavelet Scattering Transform (WST) that prioritizes information content, allowing users to customize the shape of embeddings according to their model requirements. Coupling WST with AutoEncoders (WST-AE) in a residual manner enhances semantic latent space representations, which can be clustered segment-wise in an unsupervised manner. Testing on AMI and VoxConverse datasets has shown a reduction in Diarization Error Rate (DER) with fewer training parameters and without the need for separate embedding models.