Centre for Language and Speech Technology, Radboud University Nijmegen, P.O. Box 9103, 6500 HD Nijmegen, The Netherlands.
J Acoust Soc Am. 2009 Dec;126(6):3227-35. doi: 10.1121/1.3243291.
Articulatory and acoustic reduction can manifest itself in the temporal and spectral domains. This study introduces a measure of spectral reduction, which is based on the speech decoding techniques commonly used in automatic speech recognizers. Using data for four frequent Dutch affixes from a large corpus of spontaneous face-to-face conversations, it builds on an earlier study examining the effects of lexical frequency on durational reduction in spoken Dutch [Pluymaekers, M. et al. (2005). J. Acoust. Soc. Am. 118, 2561-2569], and compares the proposed measure of spectral reduction with duration as a measure of reduction. The results suggest that the spectral reduction scores capture other aspects of reduction than duration. While duration can--albeit to a moderate degree--be predicted by a number of linguistically motivated variables (such as word frequency, segmental context, and speech rate), the spectral reduction scores cannot. This suggests that the spectral reduction scores capture information that is not directly accounted for by the linguistically motivated variables. The results also show that the spectral reduction scores are able to predict a substantial amount of the variation in duration that the linguistically motivated variables do not account for.
发音和声学的减少可以在时间和频谱域中表现出来。本研究介绍了一种基于语音解码技术的频谱减少度量方法，该技术常用于自动语音识别器中。本研究使用了来自大规模自然对话语料库中的四个常见荷兰语后缀的数据，它基于早期研究的基础上进行了扩展，该研究考察了词汇频率对荷兰语口语中时长减少的影响[Pluymaekers, M.等人（2005）。J. Acoust. Soc. Am. 118, 2561-2569]，并将所提出的频谱减少度量与时长作为减少的度量进行了比较。研究结果表明，频谱减少分数可以捕捉到时长以外的其他减少方面。虽然时长可以（尽管只是适度程度）由许多语言驱动的变量（如词汇频率、音段上下文和语速）来预测，但频谱减少分数不能。这表明频谱减少分数可以捕捉到语言驱动变量无法直接解释的信息。研究结果还表明，频谱减少分数能够预测语言驱动变量无法解释的时长变化的很大一部分。
