feat(audio): implement noise and nonsense input detection system

[jiangzhuo]

Problem Description

When users input noise or nonsensical sounds, the large language model still responds with some text, but users don't know what the response content is. There is currently a lack of effective handling mechanisms for this situation.

Current Behavior

• Users input noise or meaningless sounds
• ASR system still attempts transcription
• LLM receives low-quality input and still generates responses
• Users cannot determine the content and accuracy of system responses

Expected Behavior

The system should be able to detect and handle low-quality or meaningless input to provide a better user experience.

Possible Solutions

1. Confidence and Rationality Detection

• ASR Level: Implement speech recognition confidence detection
• LLM Level: Implement input text rationality detection
• Prompt users to re-input when confidence is below threshold

2. Audio Quality Improvement

• Implement audio noise reduction processing
• Improve input audio clarity
• Audio quality pre-detection

3. User Feedback Mechanism

• Alert users when low-clarity audio is detected
• Provide re-recording options
• Display confidence scores for user reference

Technical Implementation Considerations

• ASR confidence threshold configuration
• Text rationality evaluation algorithm
• Audio noise reduction algorithm integration
• User interface feedback design
• Configuration options allowing users to adjust sensitivity

Priority

Medium - This feature can significantly improve user experience and reduce misunderstandings caused by input quality issues

Suggested Labels

• enhancement
• audio
• ai
• user-experience