  In many speech-enabled human-machine interaction scenarios, user speech can
overlap with the device playback audio. In these instances, the performance of
tasks such as keyword-spotting (KWS) and device-directed speech detection (DDD)
can degrade significantly. To address this problem, we propose an implicit
acoustic echo cancellation (iAEC) framework where a neural network is trained
to exploit the additional information from a reference microphone channel to
learn to ignore the interfering signal and improve detection performance. We
study this framework for the tasks of KWS and DDD on, respectively, an
augmented version of Google Speech Commands v2 and a real-world Alexa device
dataset. Notably, we show a 56% reduction in false-reject rate for the DDD task
during device playback conditions. We also show comparable or superior
performance over a strong end-to-end neural echo cancellation + KWS baseline
for the KWS task with an order of magnitude less computational requirements.
