Title: Data Augmentation for Robust Keyword Spotting under Playback Interference,
Abstract: Accurate on-device keyword spotting (KWS) with low false accept and false
reject rate is crucial to customer experience for far-field voice control of
conversational agents. It is particularly challenging to maintain low false
reject rate in real world conditions where there is (a) ambient noise from
external sources such as TV, household appliances, or other speech that is not
directed at the device (b) imperfect cancellation of the audio playback from
the device, resulting in residual echo, after being processed by the Acoustic
Echo Cancellation (AEC) system. In this paper, we propose a data augmentation
strategy to improve keyword spotting performance under these challenging
conditions. The training set audio is artificially corrupted by mixing in music
and TV/movie audio, at different signal to interference ratios. Our results
show that we get around 30-45% relative reduction in false reject rates, at a
range of false alarm rates, under audio playback from such devices.