RawNet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification

Understand

Recently, direct modeling of raw waveforms using deep neural networks has been widely studied for a number of tasks in audio domains.

• In speaker verification, however, utilization of raw waveforms is in its preliminary phase, requiring further investigation.
• In this study, we explore end-to-end deep neural networks that input raw waveforms to improve various aspects: front-end speaker embedding extraction including model architecture, pre-training scheme, additional objective functions, and back-end classification.
• Adjustment of model architecture using a pre-training scheme can extract speaker embeddings, giving a significant improvement in performance.