Measuring the naturalness of text-to-speech (TTS) systems objectively is challenging because naturalness itself is a subjective, multifaceted concept. Natural speech involves nuances like intonation, rhythm, stress, and pacing, which are difficult to quantify. While subjective evaluations (e.g., human rating scales) directly capture listener perceptions, objective metrics struggle to align with these human judgments. For example, metrics like Mel-Cepstral Distortion (MCD) measure acoustic similarity to a reference recording but fail to account for prosody or contextual appropriateness. A TTS system might produce acoustically accurate but monotonous speech, which humans would rate as unnatural, yet MCD scores might suggest high quality. This mismatch highlights the limitation of relying solely on technical similarity without capturing perceptual factors.
Another challenge is the lack of standardized, context-aware metrics. Naturalness depends on context: a conversational tone for a voice assistant differs from the expressive delivery required for an audiobook. Objective metrics often ignore these situational requirements. For instance, a TTS system optimized for word-level clarity might score well on intelligibility tests but sound robotic in longer sentences due to unnatural pauses or inconsistent emphasis. Additionally, linguistic diversity complicates universal metrics. A metric designed for English might not handle tonal languages like Mandarin, where pitch variations directly affect meaning. Even within a language, regional accents or speaking styles introduce variability that static metrics cannot address without extensive adaptation.
Finally, reference-based metrics are inherently limited. Many objective measures require a "ground truth" human recording for comparison, assuming it represents ideal naturalness. However, human speech varies widely—even the same speaker might produce different renditions of the same text. This variability makes it hard to define a single reference. Reference-free metrics, which assess naturalness without comparisons, are underdeveloped and often rely on machine learning models trained on subjective data. These models may inherit biases from their training datasets or fail to generalize to unseen TTS systems or languages. For example, a model trained on studio-recorded speech might misjudge a system optimized for noisy environments. Until metrics better emulate human perceptual processes, objectively measuring TTS naturalness will remain an unsolved problem.