Mathur, A;
Isopoussu, A;
Smith, R;
Lane, ND;
Kawsar, F;
Berthouze, N;
(2018)
On robustness of cloud speech APIs: An early characterization.
In:
UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers.
(pp. pp. 1409-1413).
Association for Computing Machinery (ACM): New York, NY, USA.
Preview |
Text
mathur_hasca2018.pdf - Published Version Download (2MB) | Preview |
Abstract
The robustness and consistency of sensory inference models under changing environmental conditions and hardware is a crucial requirement for the generalizability of recent innovative work, particularly in the field of deep learning, from the lab to the real world. We measure the extent to which current speech recognition cloud models are robust to background noise, and show that hardware variability is still a problem for real-world applicability of state-of-the-art speech recognition models.
Archive Staff Only
 |
View Item |
