Abstract
Automatic speech recognition (ASR) models are typically designed to operate on a single input data type, e.g. a single or multi-channel audio streamed from a device. This design decision assumes the \textit{primary} input data source does not change and if an additional (\textit{auxiliary}) data source is occasionally available, it cannot be used. An ASR model that operates on both primary and auxiliary data can achieve better accuracy compared to a primary-only solution; and a model that can serve both \textit{primary-only} (PO) and \textit{primary-plus-auxiliary} (PPA) modes is highly desirable. In this work, we propose a unified ASR model that can serve both modes. We demonstrate its efficacy in a realistic scenario where a set of devices typically stream a single primary audio channel, and two additional auxiliary channels \textit{only when} upload bandwidth allows it. The architecture enables a unique methodology that uses both types of input audio during training time. Our proposed approach achieves up to 12.5\% relative word-error-rate reduction (WERR) compared to a PO baseline, and up to 16.0\% relative WERR in low-SNR conditions. The unique training methodology achieves up to 2.5\% relative WERR compared to a PPA baseline.
Abstract (translated)
URL
https://arxiv.org/abs/2106.02750
