Voice Based Authentication

• Voice Authentication Application created using a Siamese Network Approach.

• Dataset Used: Vox-Celeb1 Indian

• Trained three variants of the model:

  • 1.4M Parameters on 100K samples.
  • 3M Parameters on 10K samples.
  • 900K Parameters on 1M samples

• Preprocessed the audio dataset using Librosa with Fast Fourier Transform to extract vocal features.

• Further used torch to preprocess audio in real time.

• Created a novel siamese model architecture for extraction of audio features to identify speaker and verify speaker while keeping low overhead and computational delay.

• Improved the model accuracy to around 90%.

• 📝 Currently working on detailing the architectural optimizations achieved by globally caching hann windows and embedding the mel spectrograms into 450*80 matrices through a research paper.