Improving vision-inspired keyword spotting using dynamic module skipping in streaming conformer encoder
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929296883318784 |
|---|---|
| author | Bittar, Alexandre Dixon, Paul Samragh, Mohammad Nishu, Kumari Naik, Devang |
| author_facet | Bittar, Alexandre Dixon, Paul Samragh, Mohammad Nishu, Kumari Naik, Devang |
| contents | Using a vision-inspired keyword spotting framework, we propose an architecture with input-dependent dynamic depth capable of processing streaming audio. Specifically, we extend a conformer encoder with trainable binary gates that allow us to dynamically skip network modules according to the input audio. Our approach improves detection and localization accuracy on continuous speech using Librispeech top-1000 most frequent words while maintaining a small memory footprint. The inclusion of gates also reduces the average amount of processing without affecting the overall performance. These benefits are shown to be even more pronounced using the Google speech commands dataset placed over background noise where up to 97% of the processing is skipped on non-speech inputs, therefore making our method particularly interesting for an always-on keyword spotter. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_00140 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Improving vision-inspired keyword spotting using dynamic module skipping in streaming conformer encoder Bittar, Alexandre Dixon, Paul Samragh, Mohammad Nishu, Kumari Naik, Devang Sound Computer Vision and Pattern Recognition Machine Learning Audio and Speech Processing Using a vision-inspired keyword spotting framework, we propose an architecture with input-dependent dynamic depth capable of processing streaming audio. Specifically, we extend a conformer encoder with trainable binary gates that allow us to dynamically skip network modules according to the input audio. Our approach improves detection and localization accuracy on continuous speech using Librispeech top-1000 most frequent words while maintaining a small memory footprint. The inclusion of gates also reduces the average amount of processing without affecting the overall performance. These benefits are shown to be even more pronounced using the Google speech commands dataset placed over background noise where up to 97% of the processing is skipped on non-speech inputs, therefore making our method particularly interesting for an always-on keyword spotter. |
| title | Improving vision-inspired keyword spotting using dynamic module skipping in streaming conformer encoder |
| topic | Sound Computer Vision and Pattern Recognition Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2309.00140 |