  Spoken language understanding systems using audio-only data are gaining
popularity, yet their ability to handle unseen intents remains limited. In this
study, we propose a generalized zero-shot audio-to-intent classification
framework with only a few sample text sentences per intent. To achieve this, we
first train a supervised audio-to-intent classifier by making use of a
self-supervised pre-trained model. We then leverage a neural audio synthesizer
to create audio embeddings for sample text utterances and perform generalized
zero-shot classification on unseen intents using cosine similarity. We also
propose a multimodal training strategy that incorporates lexical information
into the audio representation to improve zero-shot performance. Our multimodal
training approach improves the accuracy of zero-shot intent classification on
unseen intents of SLURP by 2.75% and 18.2% for the SLURP and internal
goal-oriented dialog datasets, respectively, compared to audio-only training.
