How do you assess the performance of a TTS system across different devices?

Assessing the performance of a text-to-speech (TTS) system across devices requires evaluating how hardware, software, and environmental factors influence output quality. Key factors include the device’s processing power, audio hardware (e.g., speakers, DACs), operating system audio pipelines, and network conditions for cloud-based systems. For example, a low-end smartphone might struggle with real-time synthesis due to limited CPU resources, leading to latency or artifacts, while a high-end desktop could handle the same model smoothly. Differences in speaker quality—like a smart speaker versus budget headphones—can also mask or exaggerate issues like background noise or unnatural prosody.

To systematically test performance, use a mix of objective metrics and subjective evaluations. Objective measures include word error rate (WER) to check transcription accuracy, mean opinion score (MOS) surveys for perceived naturalness, and tools like PESQ (Perceptual Evaluation of Speech Quality) to quantify audio fidelity. For cross-device testing, run the same audio samples through each device’s playback system and record outputs using calibrated microphones in controlled environments. For example, generate a standardized set of phrases, play them on a smartphone, smart speaker, and laptop, then analyze discrepancies in timing, pitch, or clarity. Automation frameworks like pytest can streamline repeated testing across platforms.

Finally, account for real-world usage scenarios. Test under varying network conditions (e.g., 3G vs. Wi-Fi) for cloud-based TTS, and evaluate how background noise or device-specific audio enhancements (like EQ presets) affect output. For instance, a car infotainment system might apply bass boosting that distorts synthetic voices. Use tools like Audacity or MATLAB to analyze frequency responses and identify device-specific anomalies. Document findings in a matrix that maps metrics to devices, highlighting patterns like consistent latency on low-RAM devices or muffled audio on certain speakers. This structured approach helps prioritize optimizations, such as model compression for resource-constrained hardware or acoustic adjustments for specific playback environments.