Backchannels vs interruptions: Handling listener signals in voice agents

Voice agents using energy-based voice activity detection often mistake listener backchannels such as “uh-huh” and “mhmm” for interruptions, causing unnecessary stops, while failing to distinguish them from genuine corrections or attempts to take the floor. The proposed approach uses transcript-aware turn detection, filler-word recognition, token classification, and a short post-token wait window: standalone affirmative backchannels should generally let the agent continue, negative forms such as “uh-uh,” “nuh-uh,” and “mm-mm” should prompt an immediate yield, and affirmative tokens followed by additional speech should also be treated as a turn bid. Because ambiguous words like “yeah,” “right,” and “okay” depend heavily on prosody that may not be available in speech-to-text outputs, the guidance recommends relying on token identity, timestamps, continued speech, and contextual turn-detection models such as Flux STT rather than simple lookup rules alone. It also advises logging each interruption event, transcript, policy decision, and subsequent caller behavior to measure false-interruption rates and missed corrections, establish a baseline before tuning, and adjust policies according to the cost of errors in different conversational stages, languages, and use cases.

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