  Millions of people reach out to digital assistants such as Siri every day,
asking for information, making phone calls, seeking assistance, and much more.
The expectation is that such assistants should understand the intent of the
users query. Detecting the intent of a query from a short, isolated utterance
is a difficult task. Intent cannot always be obtained from speech-recognized
transcriptions. A transcription driven approach can interpret what has been
said but fails to acknowledge how it has been said, and as a consequence, may
ignore the expression present in the voice. Our work investigates whether a
system can reliably detect vocal expression in queries using acoustic and
paralinguistic embedding. Results show that the proposed method offers a
relative equal error rate (EER) decrease of 60% compared to a bag-of-word based
system, corroborating that expression is significantly represented by vocal
attributes, rather than being purely lexical. Addition of emotion embedding
helped to reduce the EER by 30% relative to the acoustic embedding,
demonstrating the relevance of emotion in expressive voice.
