  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.
