  In this paper, we describe SpeakerStew - a hybrid system to perform speaker
verification on 46 languages. Two core ideas were explored in this system: (1)
Pooling training data of different languages together for multilingual
generalization and reducing development cycles; (2) A novel triage mechanism
between text-dependent and text-independent models to reduce runtime cost and
expected latency. To the best of our knowledge, this is the first study of
speaker verification systems at the scale of 46 languages. The problem is
framed from the perspective of using a smart speaker device with interactions
consisting of a wake-up keyword (text-dependent) followed by a speech query
(text-independent). Experimental evidence suggests that training on multiple
languages can generalize to unseen varieties while maintaining performance on
seen varieties. We also found that it can reduce computational requirements for
training models by an order of magnitude. Furthermore, during model inference
on English data, we observe that leveraging a triage framework can reduce the
number of calls to the more computationally expensive text-independent system
by 73% (and reduce latency by 59%) while maintaining an EER no worse than the
text-independent setup.
