  Current state-of-the-art automatic speech recognition systems are trained to
work in specific `domains', defined based on factors like application, sampling
rate and codec. When such recognizers are used in conditions that do not match
the training domain, performance significantly drops. This work explores the
idea of building a single domain-invariant model for varied use-cases by
combining large scale training data from multiple application domains. Our
final system is trained using 162,000 hours of speech. Additionally, each
utterance is artificially distorted during training to simulate effects like
background noise, codec distortion, and sampling rates. Our results show that,
even at such a scale, a model thus trained works almost as well as those
fine-tuned to specific subsets: A single model can be robust to multiple
application domains, and variations like codecs and noise. More importantly,
such models generalize better to unseen conditions and allow for rapid
adaptation -- we show that by using as little as 10 hours of data from a new
domain, an adapted domain-invariant model can match performance of a
domain-specific model trained from scratch using 70 times as much data. We also
highlight some of the limitations of such models and areas that need addressing
in future work.
