A confidence score is a numerical measure indicating how certain an AI system is about its interpretation or output. In voice AI, confidence scores apply to speech recognition accuracy, intent classification, and entity extraction.
How are confidence scores used?
When ASR transcribes speech, it assigns confidence scores to words or phrases. Low confidence may trigger clarification requests. For intent detection, confidence thresholds determine whether to proceed with an action or ask the caller to confirm. Scores enable graceful handling of uncertainty.
Why do confidence scores matter?
Not all AI interpretations are equal. Confidence scores let the system behave differently based on certainty. High confidence enables immediate action; low confidence prompts verification. This prevents errors from propagating through the conversation when the AI is uncertain about what it heard or understood.
Confidence score in practice
A caller provides a phone number for callback. The ASR transcribes it with 95% confidence. The agent repeats the number for confirmation. On another call, the ASR transcribes a phone number with only 70% confidence due to background noise. The agent says “I want to make sure I have that right” and asks the caller to repeat it.