ISCA Archive Interspeech 2024
ISCA Archive Interspeech 2024

Exploiting Wavelet Scattering Transform for an Unsupervised Speaker Diarization in Deep Neural Network Framework

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.