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The Effects of Phonological Neighborhoods on Pronunciation Variation in Conversational Speech.

机译:语音邻域对会话语音发音变化的影响。

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摘要

This dissertation investigates the effects of phonological neighborhoods on pronunciation variation in conversational speech. Phonological neighbors are defined as words that are different in one and only one phoneme by addition, deletion and substitution. Phonological neighborhood density refers to the number of neighbors a certain word has.;Previous research has shown that phonological neighbors impede auditory perception, but facilitate lexical production. As a result, words from dense neighborhoods are harder to perceive, but easier to produce, than words from sparse neighborhoods. Following these effects, two opposite hypotheses can be formed regarding the effects of neighborhood density on pronunciation variation. The listener-oriented hypothesis predicts that high-density words will be hyperarticulated, in order to compensate for their perceptual difficulty. But the speaker-oriented hypothesis predicts that high-density words are more likely to undergo speech reduction, as other words that are easy to produce (such as high-frequency words). To test these hypotheses, two statistical models are constructed to investigate neighborhood effects on pronunciation variation in CVC monomorphemic content words. All speech data come from the Buckeye Corpus of Conversational Speech (Pitt, Dilley, Johnson, Kiesling, Raymond, Hume, and Fosler-Lussier 2007), which contains audio recordings of 40 speaker's free-form interviews. The dataset for the current research consists of more than 500 target words, represented by over 13,000 tokens. The outcome variables in the two models are word duration and degree of vowel dispersion, respectively. Neighborhood density and average neighbor frequency are the critical predictors in both models. Other factors that might affect pronunciation variation in conversational speech, such as word frequency and predictability, are entered into the models as control factors.;The results of the model analyses show that everything else being equal, high-density words are realized with shorter durations and more centralized vowels than low-density words. These findings provide strong evidence for the speaker-oriented hypothesis. A peripheral finding in the current research is a tendency for words with high-frequency neighbors to have more dispersed vowels. However, this effect is only significant when neighbor frequency from the Hoosier mental lexicon (Nusbaum, Pisoni, and Davis 1984) is used, but not when neighbor frequency computed from the CELEX database (Baayen, Piepenbrock, and Rijn 1993) is used.;Overall, major findings of the current research support the speaker-oriented hypothesis over the listener-oriented hypothesis, which suggests that word-level pronunciation variation is more heavily influenced by features of the speaker's own production system than by the consideration of listeners' needs.
机译:本文研究了语音邻域对会话语音中发音变化的影响。语音邻居被定义为通过添加,删除和替换而在一个音素和唯一一个音素上不同的单词。语音邻域密度是指某个单词具有的邻域数量。先前的研究表明,语音邻域会阻碍听觉感知,但会促进词汇产生。结果,与稀疏社区的单词相比,密集社区的单词更难感知,但更容易产生。在这些影响之后,关于邻域密度对发音变化的影响,可以形成两个相反的假设。面向听众的假设预测高密度单词将被过度发音,以补偿其感知困难。但是,以说话者为中心的假设预测,高密度单词更容易遭受语音还原,因为其他易于产生的单词(例如高频单词)也是如此。为了检验这些假设,构建了两个统计模型以研究邻域对CVC单态内容词中发音变化的影响。所有语音数据都来自七叶树会话语音语料库(Pitt,Dilley,Johnson,Kiesling,Raymond,Hume和Fosler-Lussier 2007),其中包含40位发言人的自由访谈形式的录音。当前研究的数据集由500多个目标词组成,由13,000多个令牌表示。两个模型中的结果变量分别是单词持续时间和元音散布程度。邻居密度和邻居平均频率是两个模型中的关键预测指标。将可能影响会话语音发音变化的其他因素(例如词频和可预测性)作为控制因素输入模型;模型分析的结果表明,在其他条件相同的情况下,可以在较短的时间内实现高密度词比低密度单词更集中的元音。这些发现为说话人导向的假设提供了有力的证据。当前研究的一个外围发现是,具有高频邻居的单词趋向于具有更分散的元音。但是,这种影响仅在使用Hoosier心理词典的邻居频率(Nusbaum,Pisoni和Davis 1984)时才有意义,而在使用从CELEX数据库计算的邻居频率(Baayen,Piepenbrock和Rijn 1993)时则不明显。总体而言,当前研究的主要发现支持以说话者为导向的假设,而不是以听者为导向的假设,这表明单词水平的发音变化受说话者自身生产系统特征的影响更大,而不是考虑听者的需求。

著录项

  • 作者

    Yao, Yao.;

  • 作者单位

    University of California, Berkeley.;

  • 授予单位 University of California, Berkeley.;
  • 学科 Language Linguistics.;Speech Communication.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 217 p.
  • 总页数 217
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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