End-to-end Keyword Spotting using Xception-1d
This work addresses the need for efficient keyword spotting in conversational agents, but it is incremental as it adapts an existing method to a new domain.
The paper tackled keyword spotting by adapting the Xception algorithm to audio, achieving about 96% accuracy on 35 categories and outperforming human annotation in complex tasks.
The field of conversational agents is growing fast and there is an increasing need for algorithms that enhance natural interaction. In this work we show how we achieved state of the art results in the Keyword Spotting field by adapting and tweaking the Xception algorithm, which achieved outstanding results in several computer vision tasks. We obtained about 96\% accuracy when classifying audio clips belonging to 35 different categories, beating human annotation at the most complex tasks proposed.