Testing Guide
This guide provides a structured approach to testing AI voice agents, ensuring they perform accurately, efficiently, and reliably in real-world scenarios.
AI Voice Agent Testing Guide
This guide provides a structured approach to testing AI voice agents, ensuring they perform accurately, efficiently, and reliably in real-world scenarios.
Functional Testing
Speech Recognition (ASR) Accuracy
- Test different accents, speaking speeds, and background noise.
- Measure: Word Error Rate (WER), Sentence Error Rate (SER).
Natural Language Understanding (NLU)
- Validate intent recognition, multi-intent handling, and entity extraction.
- Measure: Intent Accuracy, F1-score, Confusion Matrix.
Conversational Flow
- Test interruptions, topic shifts, and fallback responses.
- Measure: Turn Success Rate, Conversation Completion Rate.
Text-to-Speech (TTS) Quality
- Evaluate naturalness, pronunciation, and response time.
- Measure: Mean Opinion Score (MOS), Speech Naturalness Rating.
Performance Testing
Latency Testing
- Measure response times under different network conditions.
- Measure: Average Response Time, 95th Percentile Latency.
Load & Stress Testing
- Simulate concurrent users and peak loads.
- Measure: Calls Per Second (CPS), System Utilization, Failure Rate.
Robustness Testing
Noise & Environment Handling
- Test in various background noise conditions.
- Measure: ASR Accuracy Drop in Noisy Settings.
Adversarial Input Handling
- Evaluate resilience to incomplete, mixed-language, and inappropriate speech.
- Measure: False Positive & Negative Rates for handling unexpected inputs.
User Experience (UX) Testing
Human Evaluation
- Conduct real-user tests and assess clarity and engagement.
- Measure: NPS, CSAT, Call Completion Rate.
A/B Testing
- Compare different conversation flows and TTS variations.
- Measure: User Retention, Engagement Rate.
Continuous Monitoring & Improvement
Real-Time Logging
- Track ASR errors, failed conversations, and response accuracy.
- Measure: Errors Per Session, Unresolved Queries.
Feedback & Iteration
- Collect user feedback to refine AI responses.
- Measure: Accuracy Gains, Reduction in Fallback Responses.
Testing Guide
This guide provides a structured approach to testing AI voice agents, ensuring they perform accurately, efficiently, and reliably in real-world scenarios.
AI Voice Agent Testing Guide
This guide provides a structured approach to testing AI voice agents, ensuring they perform accurately, efficiently, and reliably in real-world scenarios.
Functional Testing
Speech Recognition (ASR) Accuracy
- Test different accents, speaking speeds, and background noise.
- Measure: Word Error Rate (WER), Sentence Error Rate (SER).
Natural Language Understanding (NLU)
- Validate intent recognition, multi-intent handling, and entity extraction.
- Measure: Intent Accuracy, F1-score, Confusion Matrix.
Conversational Flow
- Test interruptions, topic shifts, and fallback responses.
- Measure: Turn Success Rate, Conversation Completion Rate.
Text-to-Speech (TTS) Quality
- Evaluate naturalness, pronunciation, and response time.
- Measure: Mean Opinion Score (MOS), Speech Naturalness Rating.
Performance Testing
Latency Testing
- Measure response times under different network conditions.
- Measure: Average Response Time, 95th Percentile Latency.
Load & Stress Testing
- Simulate concurrent users and peak loads.
- Measure: Calls Per Second (CPS), System Utilization, Failure Rate.
Robustness Testing
Noise & Environment Handling
- Test in various background noise conditions.
- Measure: ASR Accuracy Drop in Noisy Settings.
Adversarial Input Handling
- Evaluate resilience to incomplete, mixed-language, and inappropriate speech.
- Measure: False Positive & Negative Rates for handling unexpected inputs.
User Experience (UX) Testing
Human Evaluation
- Conduct real-user tests and assess clarity and engagement.
- Measure: NPS, CSAT, Call Completion Rate.
A/B Testing
- Compare different conversation flows and TTS variations.
- Measure: User Retention, Engagement Rate.
Continuous Monitoring & Improvement
Real-Time Logging
- Track ASR errors, failed conversations, and response accuracy.
- Measure: Errors Per Session, Unresolved Queries.
Feedback & Iteration
- Collect user feedback to refine AI responses.
- Measure: Accuracy Gains, Reduction in Fallback Responses.