What are the challenges in objectively measuring TTS naturalness?

Measuring the naturalness of text-to-speech (TTS) systems objectively is challenging because naturalness is inherently subjective and influenced by human perception. Unlike technical metrics such as latency or word error rate, naturalness depends on how closely synthesized speech mirrors human speech patterns, including prosody, intonation, and rhythm. These qualities are difficult to quantify using traditional engineering metrics. For example, a TTS system might produce acoustically accurate phonemes but still sound robotic if the pacing or emphasis doesn’t match human expectations. This gap between measurable acoustic properties and perceived quality complicates the creation of universal benchmarks.

One major challenge is the lack of standardized objective metrics that align with human judgment. Metrics like Mel-Cepstral Distortion (MCD) or Short-Time Objective Intelligibility (STOI) focus on acoustic similarity or clarity but often fail to capture nuances like expressiveness or emotional tone. For instance, a TTS system might score well on MCD by closely matching spectral features of a recording, yet sound unnatural because it lacks appropriate pauses or stress in sentences. Similarly, STOI measures intelligibility but doesn’t account for prosody, which is critical for naturalness. Developers often rely on mean opinion scores (MOS) from human evaluators, but these are expensive, time-consuming, and inconsistent across studies due to varying participant backgrounds or evaluation criteria.

Another issue is the variability in speech contexts and speaker identities. Naturalness depends on context—for example, a conversational tone differs from a formal narration, and a system trained on one style may struggle with others. Additionally, speaker-specific traits like accent or vocal fry are hard to model. A metric that works for a neutral, monotone voice might not apply to a dynamic, expressive one. Even when using neural networks to predict human ratings, training data biases can skew results. For example, a model trained on North American English might undervalue prosodic patterns common in British English. Without context-aware, adaptable metrics, developers face trade-offs between generalization and specificity when optimizing for naturalness.