Improving Device Directedness Classification of Utterances With Semantic Lexical Features
Kellen Gillespie, Ioannis C. Konstantakopoulos, Xingzhi Guo, Vishal Thanvantri Vasudevan, Abhinav Sethy
Abstract
User interactions with personal assistants like Alexa, Google Home and Siri are typically initiated by a wake term or wake-word. Several personal assistants feature "follow-up" modes that allow users to make additional interactions without the need of a wakeword. For the system to only respond when appropriate, and to ignore speech not intended for it, utterances must be classified as device-directed or non-device-directed. State of the art systems have largely used acoustic features for this task, while others have used only lexical features or have added LM-based lexical features. We propose a directedness classifier that combines semantic lexical features with a lightweight acoustic feature and show it is effective in classifying directedness. The mixed-domain lexical and acoustic feature model is able to achieve 14% relative reduction of EER over a state of the art acoustic-only baseline model. Finally, we successfully apply transfer learning and semi-supervised learning to the model to improve accuracy even further.
BibTeX
@inproceedings{icassp2020_improvingdeviced,
title = {Improving Device Directedness Classification of Utterances With Semantic Lexical Features},
author = {Kellen Gillespie and Ioannis C. Konstantakopoulos and Xingzhi Guo and Vishal Thanvantri Vasudevan and Abhinav Sethy},
booktitle = {ICASSP 2020},
year = {2020}
}