Streaming Intended Query Detection using E2E Modeling for Continued Conversation
This work addresses the user experience issue in continued conversations for voice assistant users, though it is incremental as it builds on existing end-to-end modeling approaches.
The paper tackled the problem of reducing cognitive burden in voice-enabled applications by eliminating the need for repeated hotwords, proposing a streaming end-to-end intended query detector that improved detection accuracy by 22% relative on equal error rate and reduced latency by 600 ms compared to an independent detector.
In voice-enabled applications, a predetermined hotword isusually used to activate a device in order to attend to the query.However, speaking queries followed by a hotword each timeintroduces a cognitive burden in continued conversations. Toavoid repeating a hotword, we propose a streaming end-to-end(E2E) intended query detector that identifies the utterancesdirected towards the device and filters out other utterancesnot directed towards device. The proposed approach incor-porates the intended query detector into the E2E model thatalready folds different components of the speech recognitionpipeline into one neural network.The E2E modeling onspeech decoding and intended query detection also allows us todeclare a quick intended query detection based on early partialrecognition result, which is important to decrease latencyand make the system responsive. We demonstrate that theproposed E2E approach yields a 22% relative improvement onequal error rate (EER) for the detection accuracy and 600 mslatency improvement compared with an independent intendedquery detector. In our experiment, the proposed model detectswhether the user is talking to the device with a 8.7% EERwithin 1.4 seconds of median latency after user starts speaking.