  Agent assistance during human-human customer support spoken interactions
requires triggering workflows based on the caller's intent (reason for call).
Timeliness of prediction is essential for a good user experience. The goal is
for a system to detect the caller's intent at the time the agent would have
been able to detect it (Intent Boundary). Some approaches focus on predicting
the output offline, i.e. once the full spoken input (e.g. the whole
conversational turn) has been processed by the ASR system. This introduces an
undesirable latency in the prediction each time the intent could have been
detected earlier in the turn. Recent work on voice assistants has used
incremental real-time predictions at a word-by-word level to detect intent
before the end of a command. Human-directed and machine-directed speech however
have very different characteristics. In this work, we propose to apply a method
developed in the context of voice-assistant to the problem of online real time
caller's intent detection in human-human spoken interactions. We use a dual
architecture in which two LSTMs are jointly trained: one predicting the Intent
Boundary (IB) and then other predicting the intent class at the IB. We conduct
our experiments on our private dataset comprising transcripts of human-human
telephone conversations from the telecom customer support domain. We report
results analyzing both the accuracy of our system as well as the impact of
different architectures on the trade off between overall accuracy and
prediction latency.
