Vision-Inspired Keyword Spotting Framework for Streaming Audio
Main Ideas:
- Researchers propose an architecture with input-dependent dynamic depth for processing streaming audio.
- The architecture extends a Conformer encoder with trainable binary gates that can skip network modules based on input audio.
- The approach improves detection and localization accuracy on continuous speech using Librispeech’s 1,000 most frequent words.
- The architecture maintains a small memory footprint and reduces the average amount of processing without affecting overall performance.
Author’s take:
This research presents an innovative approach to keyword spotting in streaming audio by leveraging a vision-inspired framework. The use of input-dependent dynamic depth and trainable binary gates allows for improved accuracy in speech detection and localization. This not only enhances the performance but also helps maintain a small memory footprint. Overall, this architecture has the potential to significantly enhance keyword spotting systems in various applications.