To compare text-to-speech (TTS) engines, benchmarks typically fall into three categories: objective metrics, subjective evaluations, and task-specific benchmarks. These help assess quality, performance, and suitability for specific use cases. Below is a breakdown of common benchmarks and their applications.
1. Objective Metrics Objective benchmarks use quantitative measurements to evaluate technical aspects of TTS output. Examples include:
- Mel-Cepstral Distortion (MCD): Measures spectral accuracy by comparing synthesized speech to ground-truth recordings. Lower values indicate better quality.
- Real-Time Factor (RTF): Calculates processing speed (e.g., RTF of 0.5 means generating 1 second of audio takes 0.5 seconds). Critical for real-time applications.
- Word Error Rate (WER): Uses automatic speech recognition (ASR) to transcribe TTS output and checks if words match the input text. High WER suggests pronunciation issues.
- Latency: Time taken to generate audio from text input, important for interactive applications.
These metrics are reproducible and scalable but may not fully capture human-perceived quality. For example, a low MCD doesn’t guarantee natural-sounding prosody.
2. Subjective Evaluations and Task-Specific Benchmarks Human evaluations address nuances objective metrics miss. Common methods include:
- Mean Opinion Score (MOS): Listeners rate speech quality on a scale (e.g., 1–5) for naturalness, clarity, and intonation.
- Comparative MOS (CMOS): Listeners compare two TTS systems directly. Task-specific benchmarks focus on specialized use cases. For example:
- Blizzard Challenge: Evaluates general-purpose TTS systems using standardized datasets.
- VCTK Corpus: Tests multi-speaker synthesis with diverse accents.
- Emotional Speech Datasets: Assesses models’ ability to convey emotions like happiness or anger.
These benchmarks require careful design to avoid bias but provide insights into real-world usability.
3. Practical Benchmarks for Developers Developers often prioritize integration and efficiency. Key benchmarks include:
- Inference Speed: How quickly the engine runs on target hardware (e.g., CPUs vs. GPUs).
- Memory Footprint: RAM/VRAM usage, especially for edge devices.
- Framework Compatibility: Ease of deployment in production environments (e.g., TensorFlow, PyTorch, ONNX).
- Customization Support: Ability to fine-tune voices or adapt to new languages with minimal data.
For example, a TTS engine might excel in MOS but fail on embedded devices due to high memory use. Developers should balance quality with operational constraints like latency or hardware limitations when choosing a system.