Can an AI voice agent understand filler words like “um” or “uh”?

Yes. The Brilo AI voice agent can understand caller intent even when callers use filler words such as “um” or “uh” (disfluency). The AI phone system for business uses an automatic speech recognition (ASR) and intent classification feature to focus on meaning over exact wording. Transcript cleanup and confidence scores influence when the Brilo AI voice agent asks clarifying questions or triggers a human handoff.

Contact center teams hear natural speech with pauses, repeats, and filler words. Buyers ask whether the Brilo AI voice agent will treat those fillers as noise or as meaningful input. The concern is that filler words could cause unnecessary clarification loops, incorrect intent routing, or excessive transfers to humans.

The Brilo AI voice agent combines ASR with conversational models. During a call, the Brilo AI voice agent transcribes audio into text and runs call deflection for intent classification on the transcribed utterance. The Brilo AI voice agent uses contextual signals and confidence scores to decide whether to accept an intent, ask a short clarifying question, or route the call. Brilo AI voice agent capabilities are tuned to prioritize intent and contextual cues over isolated filler words.

Brilo AI voice agent guardrails prevent unsafe or excessive behavior. Typical guardrails include a minimum confidence threshold for automated actions, restricted topics that always require human review, and explicit trigger phrases that force a transfer. Brilo AI voice agent configuration should avoid relying on raw transcripts alone. Use transcript cleanup settings or downstream filtering to remove filler words for analytics while keeping the live intent logic tolerant of disfluency.

A customer who says “um, yes, I want to pay my bill” is treated by the AI phone system as an affirmative business payment intent. A caller who says “uh… I’m not sure” can trigger a short clarifying prompt rather than a full transfer. For noisy environments, the Brilo AI voice agent can combine noise cancellation signals (advanced noise cancellation) with ASR confidence to reduce false negatives and minimize unnecessary follow-ups.

Human handoff for the Brilo AI voice agent is rule driven. Handoffs occur when confidence scores fall below configured thresholds, when callers explicitly request a person, or when restricted topics are detected. The Brilo AI voice agent includes context in the handoff summary so the human receives collected details and a brief intent history. Adjusting the tolerance of handoff conditions reduces transfers caused only by filler words.

To optimize filler handling, supply representative audio and transcripts that include natural speech and filler words. Provide accepted response examples and edge cases in the agent’s knowledge base (training data). Configure prompt tuning (initial instructions) so the Brilo AI call intelligence focuses on intent rather than literal tokens. Define confidence score thresholds and transfer rules in Actions. If you operate in noisy locations, enable advanced noise cancellation and test with real recordings.

When the Brilo AI voice agent handles disfluency correctly, teams see fewer unnecessary clarifications and fewer avoidable transfers. The AI business phone system improves average handle time for routine calls and reduces agent workload for high-volume intents. Analytics are cleaner when transcript cleanup (post-processing) removes filler words for reporting while live intent detection remains tolerant to natural speech.

Run staged test calls that include filler words, regional phrasing, and real-world noise. Use the AI business phone system's call logs to inspect transcripts, intent classification, and confidence scores. For implementation patterns and escalation practices, review our resources on call deflection and intent detection. For guided support, book a call with our team today.