Device-directed Utterance Detection
Sri Harish Mallidi, Roland Maas, Kyle Goehner, Ariya Rastrow, Spyros Matsoukas, Björn Hoffmeister · 2018
In this work, we propose a classifier for distinguishing device-directed queries from background speech in the context of interactions with voice assistants.Applications include rejection of false wake-ups or unintended interactions as well as enabling wake-word free followup queries.Consider the example interaction: "Computer, play music", "Computer, reduce the volume".In this interaction, the user needs to repeat the wake-word (Computer) for the second query.To allow for more natural interactions, the device could immediately re-enter listening state after the first query (without wake-word repetition) and accept or reject a potential follow-up as device-directed or background speech.The proposed model consists of two long short-term memory (LSTM) neural networks trained on acoustic features and automatic speech recognition (ASR) 1-best hypotheses, respectively.A feed-forward deep neural network (DNN) is then trained to combine the acoustic and 1-best embeddings, derived from the LSTMs, with features from the ASR decoder.Experimental results show that ASR decoder, acoustic embeddings, and 1-best embeddings yield an equal-error-rate (EER) of 9.3 %, 10.9 % and 20.1 %, respectively.Combination of the features resulted in a 44 % relative improvement and a final EER of 5.2 %.