Real-time Caller Intent Detection In Human-Human Customer Support Spoken Conversations

• URL: http://arxiv.org/abs/2208.06802v1
• Date: Sun, 14 Aug 2022 07:50:23 GMT
• Title: Real-time Caller Intent Detection In Human-Human Customer Support Spoken Conversations
• Authors: Mrinal Rawat, Victor Barres
• Abstract summary: Agent assistance during human-human customer support spoken interactions requires triggering based on the caller's intent (reason for call) 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) 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.
• Score: 10.312382727352823
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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