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

Implicit Acoustic Echo Cancellation for Keyword Spotting and Device-Directed Speech Detection