  Device-directed speech detection (DDSD) is the binary classification task of
distinguishing between queries directed at a voice assistant versus side
conversation or background speech. State-of-the-art DDSD systems use verbal
cues, e.g acoustic, text and/or automatic speech recognition system (ASR)
features, to classify speech as device-directed or otherwise, and often have to
contend with one or more of these modalities being unavailable when deployed in
real-world settings. In this paper, we investigate fusion schemes for DDSD
systems that can be made more robust to missing modalities. Concurrently, we
study the use of non-verbal cues, specifically prosody features, in addition to
verbal cues for DDSD. We present different approaches to combine scores and
embeddings from prosody with the corresponding verbal cues, finding that
prosody improves DDSD performance by upto 8.5% in terms of false acceptance
rate (FA) at a given fixed operating point via non-linear intermediate fusion,
while our use of modality dropout techniques improves the performance of these
models by 7.4% in terms of FA when evaluated with missing modalities during
inference time.
