Efficient Multimodal Neural Networks for Trigger-less Voice Assistants

• URL: http://arxiv.org/abs/2305.12063v1
• Date: Sat, 20 May 2023 02:52:02 GMT
• Title: Efficient Multimodal Neural Networks for Trigger-less Voice Assistants
• Authors: Sai Srujana Buddi, Utkarsh Oggy Sarawgi, Tashweena Heeramun, Karan Sawnhey, Ed Yanosik, Saravana Rathinam, Saurabh Adya
• Abstract summary: We propose a neural network based audio-gesture multimodal fusion system for smartwatches. The system better understands temporal correlation between audio and gesture data, leading to precise invocations. It is lightweight and deployable on low-power devices, such as smartwatches, with quick launch times.
• Score: 0.8209843760716959
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The adoption of multimodal interactions by Voice Assistants (VAs) is growing rapidly to enhance human-computer interactions. Smartwatches have now incorporated trigger-less methods of invoking VAs, such as Raise To Speak (RTS), where the user raises their watch and speaks to VAs without an explicit trigger. Current state-of-the-art RTS systems rely on heuristics and engineered Finite State Machines to fuse gesture and audio data for multimodal decision-making. However, these methods have limitations, including limited adaptability, scalability, and induced human biases. In this work, we propose a neural network based audio-gesture multimodal fusion system that (1) Better understands temporal correlation between audio and gesture data, leading to precise invocations (2) Generalizes to a wide range of environments and scenarios (3) Is lightweight and deployable on low-power devices, such as smartwatches, with quick launch times (4) Improves productivity in asset development processes.

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