  Recent innovations in self-supervised representation learning have led to
remarkable advances in natural language processing. That said, in the speech
processing domain, self-supervised representation learning-based systems are
not yet considered state-of-the-art. We propose leveraging recent advances in
self-supervised-based speech processing to create a common speech analysis
engine. Such an engine should be able to handle multiple speech processing
tasks, using a single architecture, to obtain state-of-the-art accuracy. The
engine must also enable support for new tasks with small training datasets.
Beyond that, a common engine should be capable of supporting distributed
training with client in-house private data. We present the architecture for a
common speech analysis engine based on the HuBERT self-supervised speech
representation. Based on experiments, we report our results for language
identification and emotion recognition on the standard evaluations NIST-LRE 07
and IEMOCAP. Our results surpass the state-of-the-art performance reported so
far on these tasks. We also analyzed our engine on the emotion recognition task
using reduced amounts of training data and show how to achieve improved
results.
