  The Automated Speech Recognition (ASR) community experiences a major turning
point with the rise of the fully-neural (End-to-End, E2E) approaches. At the
same time, the conventional hybrid model remains the standard choice for the
practical usage of ASR. According to previous studies, the adoption of E2E ASR
in real-world applications was hindered by two main limitations: their ability
to generalize on unseen domains and their high operational cost. In this paper,
we investigate both above-mentioned drawbacks by performing a comprehensive
multi-domain benchmark of several contemporary E2E models and a hybrid
baseline. Our experiments demonstrate that E2E models are viable alternatives
for the hybrid approach, and even outperform the baseline both in accuracy and
in operational efficiency. As a result, our study shows that the generalization
and complexity issues are no longer the major obstacle for industrial
integration, and draws the community's attention to other potential limitations
of the E2E approaches in some specific use-cases.
