使用嵌入预测普通话单音节词的口语持续时间和音高
Using embeddings to predict spoken word duration and pitch in Mandarin monosyllabic words
AI总结：
研究利用上下文嵌入预测普通话单音节词的口语持续时间，发现嵌入在类型和个体词元层面均能显著预测时长，且预测精度足以将归一化f0轮廓反变换到毫秒时间尺度。
摘要：
已有研究表明，会话语音中普通话词语的时间归一化f0轮廓部分可从其上下文嵌入（CEs）预测。本研究调查CEs是否也能预测从自发言语语料库中提取的7470个普通话单音节CV词的口语持续时间。我们通过类型和词元置换基线结果表明，CEs确实能显著预测持续时间，不仅在类型层面，而且在个体词元层面。我们还表明，预测的持续时间足够精确，可以将[0,1]归一化时间内的预测f0轮廓反变换到毫秒时间尺度。得到的预测轮廓近似于经验轮廓，并且也优于置换基线。
英文摘要：
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 words extracted from a Mandarin corpus of spontaneous speech. We show that CEs indeed are predictive for duration, above chance level, not only at the type level, but also at the level of individual tokens, as indicated by the results obtained with the type-wise and token-wise permutation baselines. We also show that the predicted durations are sufficiently precise to back-transform predicted f0 contours in [0,1] normalized time to contours on the ms time scale. The resulting predicted contours approximate empirical contours and also outperform a permutation baseline.
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。
