通过条件预测离散令牌对语音合成的语调和可懂性进行客观评估
Objective Evaluation of Prosody and Intelligibility in Speech Synthesis via Conditional Prediction of Discrete Tokens
AI总结：
本文提出TTScore，一种基于条件预测离散语音令牌的无参考评估框架，用于更准确地评估语音合成的语调和可懂性。
摘要：
对合成语音进行客观评估对于推进语音生成系统至关重要，但现有的可懂性和语调度量标准在范围上有限且与人类感知弱相关。词错误率（WER）只提供了一个基于文本的粗略可懂性度量，而F0-RMSE及相关基于音高的度量则提供了一个狭窄且依赖参考的语调视角。为了解决这些限制，我们提出了TTScore，一种基于条件预测离散语音令牌的定向且无参考的评估框架。TTScore使用两个序列到序列的预测器，基于输入文本进行条件化：TTScore-int通过内容令牌测量可懂性，TTScore-pro通过语调令牌评估语调。对于每个合成的语句，预测器计算相应令牌序列的可能性，从而得到可解释的分数，这些分数捕捉了与预期语言内容和语调结构的一致性。在SOMOS、VoiceMOS和TTSArena基准测试中，实验表明TTScore-int和TTScore-pro提供了可靠、面向特定方面的评估，并在整体质量的人类判断上比现有专注于可懂性和语调的度量标准具有更强的相关性。
英文摘要：
Objective evaluation of synthesized speech is critical for advancing speech generation systems, yet existing metrics for intelligibility and prosody remain limited in scope and weakly correlated with human perception. Word Error Rate (WER) provides only a coarse text-based measure of intelligibility, while F0-RMSE and related pitch-based metrics offer a narrow, reference-dependent view of prosody. To address these limitations, we propose TTScore, a targeted and reference-free evaluation framework based on conditional prediction of discrete speech tokens. TTScore employs two sequence-to-sequence predictors conditioned on input text: TTScore-int, which measures intelligibility through content tokens, and TTScore-pro, which evaluates prosody through prosody tokens. For each synthesized utterance, the predictors compute the likelihood of the corresponding token sequences, yielding interpretable scores that capture alignment with intended linguistic content and prosodic structure. Experiments on the SOMOS, VoiceMOS, and TTSArena benchmarks demonstrate that TTScore-int and TTScore-pro provide reliable, aspect-specific evaluation and achieve stronger correlations with human judgments of overall quality than existing intelligibility and prosody-focused metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
