Chintan Agrawal and Daniel Wirjo explain why a voice agent's conversational quality depends on audio engineering around the language model. Speech recognition, generation and synthesis form only part of the response budget; endpoint detection, buffering and tail latency determine whether the system answers naturally or talks over a hesitant user.
Chintan Agrawal compares three approaches: a voice-activity detector with a silence timer, speech-provider endpoint signals, and a local semantic turn model with a silence fallback. Short timers can interrupt unfinished thoughts, while conservative endpointing can leave an awkward pause. Interruptions also need intent decisions, distinguishing a correction from a brief acknowledgement before cancelling generation and flushing queued audio.
Daniel Wirjo demonstrates these policies in a Pipecat travel-assistant pipeline. The examples expose premature completion, delayed provider responses and the limits of a model backed by a timeout rather than proving a universally correct configuration. Chintan Agrawal also stresses high-percentile latency, context growth and escalation policies as practical concerns in longer conversations.
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