SIR Beam Selector for Amazon Echo Devices Audio Front-End

Abstract

The Audio Front-End (AFE) is a key component in mitigating acoustic environmental challenges for far-field automatic speech recognition (ASR) on Amazon Echo family of products. A critical component of the AFE is the Beam Selector, which identifies which beam points to the target user. In this paper, we proposed a new SIR beam selector that utilizes subband-based signal-to-interference ratios to learn the locations of the audio sources and therefore further improve the beam selection accuracy for multi-microphone based AFE system. We analyzed the performance of a Signal to Interference Ratio (SIR) beam selector with a comparison to classic beam selector using the datasets collected under various conditions. This method is evaluated and shown to simultaneously decrease word-error-rate (WER) for speech recognition by up to 46.20% and improve barge-in performance via FRR by up to 39.18%.