研究人员证明了上下文嵌入（CEs）可以预测普通话单音节词的语音时长和音高。该研究分析了来自自发语音语料库的7470个词元，发现CEs在词语类型和词元层面都对时长具有预测性。此外，预测的时长足够精确，可以将归一化的f0轮廓精确地反向转换为毫秒级，优于置换基线。 AI
影响 这项研究通过改进时长和音高等韵律特征的预测，有望实现更自然、更准确的语音合成和分析。
排序理由 研究论文，详细介绍了一种使用嵌入式模型预测语言特征的新颖方法。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
arXiv:2607.02002v1 Announce Type: new Abstract: Time-normalized f0 contours of Mandarin words in conversational speech have been shown to be predictable in part from their contextualized embeddings (CEs). The present study investigates whether CEs also predict spoken word duratio…
Time-normalized f0 contours of Mandarin words in conversational speech have been shown to be predictable in part from their contextualized embeddings (CEs). The present study investigates whether CEs also predict spoken word duration for 7470 tokens of Mandarin monosyllabic CV wo…
Time-normalized f0 contours of Mandarin words in conversational speech have been shown to be predictable in part from their contextualized embeddings (CEs). The present study investigates whether CEs also predict spoken word duration for 7470 tokens of Mandarin monosyllabic CV wo…