What are common pitfalls in TTS evaluation?

Evaluating text-to-speech (TTS) systems effectively requires avoiding common mistakes that can lead to misleading conclusions. Three key pitfalls include over-reliance on automated metrics, insufficient attention to linguistic accuracy, and poorly designed subjective evaluations. Each of these can skew results, making it harder to assess a system’s real-world performance.

First, relying too heavily on automated metrics like Mel-Cepstral Distortion (MCD) or Word Error Rate (WER) can create a false sense of quality. These metrics measure specific technical aspects—such as spectral similarity between synthesized and reference audio or transcription accuracy—but fail to capture nuances like naturalness, emotional expression, or prosody. For example, a TTS model might achieve low MCD scores by producing spectrally clean audio that still sounds robotic or unnatural due to monotone delivery. Similarly, WER might not detect mispronunciations of homographs (e.g., “read” pronounced as “reed” instead of “red” in context) because the transcribed text matches the input. Developers should combine these metrics with human evaluations to assess aspects that algorithms cannot quantify.

Second, overlooking linguistic accuracy is a critical oversight. TTS systems must handle complex linguistic features like phoneme articulation, syllable stress, and intonation patterns. For instance, a system might correctly generate the word “record” but stress the wrong syllable (e.g., “RE-cord” instead of “re-CORD” for the verb form), altering the meaning. Similarly, handling rare words, proper nouns, or code-switching (mixing languages) often exposes weaknesses. A model trained primarily on English might mispronounce foreign names or technical terms, reducing usability in real applications. Including targeted tests for these edge cases—and using pronunciation dictionaries or linguistic rules—can help identify gaps that generic evaluations miss.

Finally, subjective evaluations are often poorly designed, leading to inconsistent or biased feedback. For example, using untrained listeners or small sample sizes can skew results, as individual preferences vary widely. Asking evaluators to rate “naturalness” without defining criteria (e.g., clarity, pacing, emotion) may yield vague or contradictory responses. To improve reliability, use structured protocols: define evaluation criteria, train listeners with example ratings, and include control samples (e.g., human speech vs. synthetic). Additionally, avoid testing in noisy environments or with inconsistent playback devices, as these factors can distort perceptions of audio quality. A well-designed subjective test balances practicality with rigor to ensure actionable insights.

By addressing these pitfalls—combining objective metrics with human judgment, testing linguistic edge cases, and refining subjective methods—developers can build more robust TTS evaluation pipelines.