Keyword spotting for Google assistant using contextual speech recognition

Abstract

We present a novel keyword spotting (KWS) system that uses contextual automatic speech recognition (ASR). For voice-activated devices, it is common that a KWS system is run on the device in order to quickly detect a trigger phrase (e.g. “Ok Google”). After the trigger phrase is detected, the audio corresponding to the voice command that follows is streamed to the server. The audio is transcribed by the server-side ASR system and semantically processed to generate a response which is sent back to the device. Due to limited resources on the device, the device KWS system might introduce false accepts (FA) and false rejects (FR) that can cause an unsatisfactory user experience. We describe a system that uses server-side contextual ASR and trigger phrase non-terminals to improve overall KWS accuracy. We show that this approach can significantly reduce the FA rate (by 89%) while minimally increasing the FR rate (by 0.2%). Furthermore, we show that this system significantly improves the ASR quality, reducing Word Error Rate (WER) (by 10% to 50% relative), and allows the user to speak seamlessly, without pausing between the trigger phrase and the voice command.