Using transcription confidence scores to improve slot filling in Amazon Lex

This article discusses how to use transcription confidence scores in Amazon Lex to improve slot filling and speech recognition accuracy in voice-enabled chatbots.

• Transcription confidence scores range from low to high, indicating speech-to-text accuracy
• Three main strategies for using confidence scores:
  • Progressive Confirmation
  • Adaptive Re-prompting
  • Conversation Flow Branching
• Benefits include:
  • Reducing information capture errors
  • Improving self-service containment rates
  • Handling challenging audio conditions
• Confidence score handling examples include:
  • High confidence (>0.9): Accept automatically
  • Medium confidence (0.6-0.9): Request user confirmation
  • Low confidence (<0.6): Request re-input

The solution provides a more natural and accurate conversational experience by dynamically adjusting bot responses based on speech recognition confidence.