  Recent advances in self-supervision have dramatically improved the quality of
speech representations. However, deployment of state-of-the-art embedding
models on devices has been restricted due to their limited public availability
and large resource footprint. Our work addresses these issues by publicly
releasing a collection of paralinguistic speech models that are small and near
state-of-the-art performance. Our approach is based on knowledge distillation,
and our models are distilled on public data only. We explore different
architectures and thoroughly evaluate our models on the Non-Semantic Speech
(NOSS) benchmark. Our largest distilled model is less than 15% the size of the
original model (314MB vs 2.2GB), achieves over 96% the accuracy on 6 of 7
tasks, and is trained on 6.5% the data. The smallest model is 1% in size (22MB)
and achieves over 90% the accuracy on 6 of 7 tasks. Our models outperform the
open source Wav2Vec 2.0 model on 6 of 7 tasks, and our smallest model
outperforms the open source Wav2Vec 2.0 on both emotion recognition tasks
despite being 7% the size.
