连续语音韵律中的信息内容、权重和分布——跨体裁比较

Helen Kai-Yun Chen, Wei-te Fang, Chiu-yu Tseng
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引用次数: 5

摘要

本研究利用不同语音体裁的数据,探讨了连续语音中信息内容的构成。我们的方法是通过从4种不同风格的数据中感知连续语音的韵律突出来衡量信息的权重、分布和相关表达性。这一替代性观点不同于已有的关于情绪相关韵律表达的研究,主要基于这样的假设:模式突出也与信息的分配和加权负载呈正相关,但仅与更高层次的话语单位呈正相关。比较了四种语言类型,即2种阅读风格和2种带有感知突出度的自发语言。信息分配和权重的计算使用突出模式的频率计数和突出水平加权分数的指定。最具启发性的结果是在自发对话的数据中发现的,由于不断减少,其特征是更多种类的强调模式。更重要的是,会话数据还显示,虽然段落级韵律单位携带的信息内容最少,但篇章级韵律单位的信息权重得分最高。换句话说,会话言语的一个主要但不太为人所知的显著特征是其信息量最大,只有在最高水平的话语韵律单位中进行研究时,这一点才会显现出来。我们相信这些结果进一步加深了我们对一般连续言语和自发对话中韵律表达的理解;并且可以很容易地应用于许多语音技术相关的实现中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Information content, weighting and distribution in continuous speech prosody - A cross-genre comparison
This study explores the composition of information content in continuous speech using data of a diversity of speech genres. Our approach is to measure information weighting, distribution and correlative expressiveness through perceived prosodic prominences in continuous speech from data of 4 different styles. This alternative perspective differs from reported studies on emotion related prosodic expressions and is based mainly on the assumption that patterned prominences are also positively correlated with the allocation and weighted loading of information, but only by higher level of discourse units. Four speech genres, i.e., 2 styles of read vs. 2 of spontaneous speech annotated with perceived prominences at 4 relative degrees are compared. Information allocation and weighting are calculated using both frequency count of prominence patterns and designation of weighting scores by prominence levels. The most revealing results are found in data of spontaneous conversation, which feature in more varieties of emphasis patterns as results of constant reduction. Far more significantly, conversation data also showcase that while their paragraph-level prosodic units carry the least amount of information content, the discourse-level prosodic units exhibit the highest score of information weighting. In other words, one major but less known distinctive feature of conversation speech is its largest amount of information content, which only surfaces when examined by the highest level of discourse-prosodic unit. We believe the results have furthered our understanding of prosody expressions in continuous speech in general and spontaneous conversation in particular; and could readily be utilized in many speech technology related implementations.
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