What are common pitfalls in TTS evaluation?
A common pitfall in TTS evaluation is over-reliance on either subjective or objective metrics without balancing their strengths. Subjective metrics like Mean Opinion Score (MOS) require human listeners to rate speech quality, but they can be inconsistent due to participant variability, fatigue, or small sample sizes. For example, a MOS of 4.0 might seem good, but without context (e.g., comparing it to established systems), it’s hard to interpret. Conversely, objective metrics like Mel-Cepstral Distortion (MCD) measure spectral accuracy but fail to capture prosody or naturalness. A system optimized for MCD might produce synthetic-sounding speech despite low distortion scores. Relying solely on one type of metric risks missing critical flaws, so combining both approaches is essential.
Another issue is using narrow or non-representative datasets. TTS systems trained and evaluated on clean, single-speaker datasets may struggle with real-world scenarios involving diverse accents, background noise, or emotional speech. For instance, a system evaluated only on news-reading data might perform poorly when generating conversational dialogue. Similarly, testing in controlled environments (e.g., quiet rooms) overlooks challenges like intelligibility in noisy settings. Evaluations must include diverse linguistic contexts, speaker variations, and environmental conditions to ensure robustness and generalizability.
Finally, ignoring linguistic and contextual factors leads to incomplete assessments. Systems might handle common phrases well but fail on rare words, complex syntax, or prosodic features like emphasis and intonation. For example, a TTS model could mispronounce homographs (e.g., “read” in past vs. present tense) or struggle with question versus statement intonation. Additionally, evaluations often overlook application-specific needs: a navigation system prioritizes clarity in short phrases, while an audiobook reader requires natural pacing. Without testing for these nuances, evaluations overestimate performance in practical use cases. Addressing these gaps requires targeted tests (e.g., stress-testing phoneme accuracy) and aligning metrics with real-world requirements.