How Shaip Delivered a Scalable Voice Cloning Quality Evaluation Program for an AI Speech Client
From demo-quality to deployment-ready — how structured human evaluation helped an AI speech client close the gap between lab metrics and real-world performance.
Project Overview
Voice cloning models can sound impressive in demos but still struggle in real-world use. The client needed a reliable way to measure whether their model was actually improving – especially for Indian English, which was a priority deployment market.
Shaip was brought in to design and manage a human evaluation program that could answer three key business questions:
- Does the speech sound natural?
- Does it still sound like the original speaker?
- Is it safe and reliable enough for production use?
Instead of relying only on automated metrics, the project used trained human reviewers to evaluate real audio outputs and identify where the model still fell short.
Key Dataset Metrics
Use Case
Voice Cloning Quality Assessment
Project Duration
12 Weeks
Samples Reviewed
12,400 Synthesized Audio Clips
Annotators Deployed
48 Trained English Evaluators
Challenges in Assessing TTS and Voice Cloning Quality
- The model needed to work well across multiple English accents, especially Indian English.
- Audio quality had to improve in ways that matter to end users, not just in lab metrics.
- The team needed a clear way to identify what was going wrong in the speech output.
- Long audio clips sometimes lost the original speaker’s identity over time.
- The client also needed checks for safety, impersonation risk, and watermark presence.
Solution: Human Evaluation Framework for AI Voice Quality
Evaluation Strategy
Shaip built a structured review framework to assess naturalness, clarity, voice similarity, consistency, and safety.
Human Review at Scale
48 trained evaluators reviewed 12,400 audio samples across Indian English, Neutral American English, and a Hinglish sub-track.
Three-Part Assessment
- Reviewers scored how natural and understandable each clip sounded.
- They compared pairs of clips to identify which version was better.
- They tagged recurring quality issues such as unnatural rhythm, pitch problems, and speaker drift.
Quality Control
Shaip used calibration tasks, gold-standard checks, repeat reviews, and QA monitoring to keep scoring consistent and reliable.
Actionable Feedback Loop
Findings from each sprint were fed back into the client’s tuning process, helping the model improve over multiple rounds.
Project Scope: Languages, Accents & Review Coverage
| Area | Scope |
|---|---|
| Language | English |
| Priority Accents | Indian English, Neutral American English |
| Secondary Coverage | British English, Hinglish sub-track |
| Sample Types | Short reference clips, few-shot samples, long-form speech |
| Review Output | Quality ratings, preference labels, issue tagging |
| Engagement Length | 12 weeks |
Outcomes: Measurable Improvements in Voice Cloning
- Clear improvement in voice quality: The model’s overall quality score improved from 3.41 to 4.12, showing that speech became more natural and production-ready.
- Better speaker matching: The system became much better at preserving the original speaker’s voice, improving similarity from 0.71 to 0.87.
- Fewer noticeable errors: Speech issues dropped from 31% of samples at baseline to 11% by the final sprint.
- Strong intelligibility: Final word error rate for Indian English reached 4.8%, beating the target threshold.
- Safer deployment readiness: The evaluation also confirmed strong performance on key safety checks, including impersonation risk screening and watermark verification.
Shaip helped us turn subjective audio quality into a measurable improvement program. Their evaluation framework gave us clear signals on what to fix, where to improve, and how to move closer to production with confidence.
– AI Speech Product Leader
