Beyond Naturalness: Probing Automated Text-To-Speech Evaluators on Linguistically Grounded Dimensions
Researchers introduce a new benchmark that evaluates TTS systems across ten linguistically grounded perceptual dimensions, using 860 utterances annotated by expert linguists.
- A new benchmark evaluates TTS output on ten linguistically defined perceptual dimensions.
- 860 utterances were annotated by expert linguists, creating a dimension‑level meta‑evaluation dataset.
- Existing MOS predictors and Audio‑LLM judges struggle to capture many of these fine‑grained speech qualities.
- The benchmark and accompanying code are released publicly to foster more detailed TTS research.
Current automated TTS evaluation methods, such as Mean Opinion Score (MOS) predictors and Audio‑LLM judges, aim to mimic human perception but often collapse all aspects of speech into a single "naturalness" score. To address this limitation, the authors propose a linguistically grounded annotation schema that separates perception into ten distinct dimensions, ranging from prosody to articulation.
The team collected 860 utterances and had trained linguists rate each one on the new schema, creating the first dimension‑level meta‑evaluation benchmark for TTS. They then tested four state‑of‑the‑art MOS predictors and two Audio‑LLM judges against this benchmark, revealing substantial gaps in how well these systems capture specific speech qualities.
Results show that while some models perform adequately on overall naturalness, they frequently miss finer‑grained attributes such as rhythm or voice timbre. The benchmark thus provides a more nuanced diagnostic tool for developers seeking to improve TTS realism beyond a single aggregate metric.
By publishing the dataset and evaluation scripts, the authors enable the community to benchmark future TTS models on these detailed dimensions, encouraging research that targets specific perceptual shortcomings.
Provides a diagnostic tool to pinpoint weaknesses in TTS models beyond overall naturalness.
Helps product teams assess speech quality on specific attributes important for user experience.
Highlights emerging evaluation standards that could differentiate next‑generation voice AI products.
Offers a concrete dataset for studying speech perception and model evaluation techniques.
Enables clearer understanding of what makes synthetic speech sound natural to human listeners.
- Mean Opinion Score (MOS)
- A subjective rating (typically 1‑5) used to measure perceived quality of audio or speech.
- Audio Large Language Model (Audio‑LLM)
- A neural model trained on audio data that can generate or evaluate speech, similar to text‑LLMs.
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