This text proposes an architecture capable of processing streaming audio using a vision-inspired keyword spotting framework. By extending a Conformer encoder with trainable binary gates, the approach improves detection and localization accuracy on continuous speech while maintaining a small memory footprint. The inclusion of gates also reduces the average amount of processing without affecting performance.
**Improving Vision-inspired Keyword Spotting Using a Streaming Conformer Encoder With Input-dependent Dynamic Depth**
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This architecture utilizes a vision-inspired keyword spotting framework to process streaming audio in real-time. By extending a Conformer encoder with trainable binary gates, our solution allows for dynamic skipping of network modules based on the input audio. This not only enhances detection and localization accuracy for continuous speech but also maintains a small memory footprint. The inclusion of gates also reduces the average amount of processing without compromising overall performance.
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