A Multimodal Approach to Device-Directed Speech Detection with Large Language Models
This addresses a usability issue for users of virtual assistants, though it is incremental as it builds on existing methods like ASR and LLMs.
The paper tackles the problem of making virtual assistant interactions more intuitive by eliminating the need for trigger phrases, achieving relative equal-error-rate improvements of up to 39% and 61% over text-only and audio-only models through a multimodal approach.
Interactions with virtual assistants typically start with a predefined trigger phrase followed by the user command. To make interactions with the assistant more intuitive, we explore whether it is feasible to drop the requirement that users must begin each command with a trigger phrase. We explore this task in three ways: First, we train classifiers using only acoustic information obtained from the audio waveform. Second, we take the decoder outputs of an automatic speech recognition (ASR) system, such as 1-best hypotheses, as input features to a large language model (LLM). Finally, we explore a multimodal system that combines acoustic and lexical features, as well as ASR decoder signals in an LLM. Using multimodal information yields relative equal-error-rate improvements over text-only and audio-only models of up to 39% and 61%. Increasing the size of the LLM and training with low-rank adaption leads to further relative EER reductions of up to 18% on our dataset.