Toward Objective and Interpretable Prosody Evaluation in Text-to-Speech: A Linguistically Motivated Approach
Abstract
Prosody is essential for speech technology, shaping comprehension, naturalness, and expressiveness. However, current text-to-speech (TTS) systems still struggle to accurately capture human-like prosodic variation, in part because existing evaluation methods for prosody remain limited. Traditional metrics like Mean Opinion Score (MOS) are resource-intensive, inconsistent, and offer little insight into why a system sounds unnatural. This study introduces a linguistically informed, semi-automatic framework for evaluating TTS prosody through a two-tier architecture that mirrors human prosodic organization. The method uses quantitative linguistic criteria to evaluate synthesized speech against human speech corpora across multiple acoustic dimensions. By integrating discrete and continuous prosodic measures, it provides objective and interpretable metrics of both event placement and cue realization, while accounting for the natural variability observed across speakers and prosodic cues. Results show strong correlations with perceptual MOS ratings while revealing model-specific weaknesses that traditional perceptual tests alone cannot capture. This approach provides a principled path toward diagnosing, benchmarking, and ultimately improving the prosodic naturalness of next-generation TTS systems.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper presents a novel framework for evaluating prosody in TTS systems, offering a linguistically informed approach that bridges the gap between subjective and objective evaluation methods. The comprehensive analysis of the technical contributions and methodology highlights its significance in advancing the field of speech synthesis.
Methodology
The proposed methodology introduces a two-tier framework for evaluating TTS prosody that integrates both discrete and continuous measures, reflecting the complexity of human prosodic expression. This approach is grounded in linguistic theory, allowing for a more nuanced evaluation of synthesized speech compared to traditional metrics like MOS. The framework's ability to account for variability among speakers and prosodic cues is a significant advancement, as it provides interpretable metrics that can diagnose specific weaknesses in TTS models.
Results & evaluation
The experiments conducted are robust, utilizing a range of TTS models and a well-defined human speech corpus. The comparison of model outputs against human performances using both perceptual evaluations (MOS and pairwise comparisons) and objective acoustic measures strengthens the findings. The results demonstrate clear correlations between the proposed metrics and human judgments, validating the effectiveness of the new evaluation framework.
Reproducibility
The paper outlines a clear methodology for data collection and analysis, including the use of specific acoustic features and statistical measures. However, the lack of a publicly accessible dataset or code repository limits the reproducibility of the findings. Future work should aim to provide these resources to facilitate further research and validation of the proposed methods.
Limitations
One limitation of the study is the reliance on a single corpus of human speech, which may not capture the full range of prosodic variability present in natural speech across different contexts and speakers. Additionally, while the framework addresses some limitations of traditional metrics, it may still be sensitive to the specific characteristics of the chosen reference corpus, potentially affecting generalizability.
Broader impact
The proposed evaluation framework has the potential to significantly impact the development of next-generation TTS systems by providing clearer diagnostic tools for improving prosodic naturalness. By moving beyond traditional evaluation methods, this work could lead to more expressive and human-like speech synthesis, enhancing applications in various fields such as virtual assistants, audiobooks, and accessibility technologies. This paper presents a novel framework for evaluating prosody in TTS systems, offering a linguistically informed approach that bridges the gap between subjective and objective evaluation methods. The comprehensive analysis of the technical contributions and methodology highlights its significance in advancing the field of speech synthesis.
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