FRILL: A Non-Semantic Speech Embedding for Mobile Devices

Understand

Learned speech representations can drastically improve performance on tasks with limited labeled data.

• However, due to their size and complexity, learned representations have limited utility in mobile settings where run-time performance can be a significant bottleneck.
• In this work, we propose a class of lightweight non-semantic speech embedding models that run efficiently on mobile devices based on the recently proposed TRILL speech embedding.
• We combine novel architectural modifications with existing speed-up techniques to create embedding models that are fast enough to run in real-time on a mobile device and exhibit minimal performance degradation on a benchmark of non-semantic speech tasks.