Chunking up speech in real time: linguistic predictors and cognitive constraints

IF 1.1 3区 心理学 0 LANGUAGE & LINGUISTICS
Svetlana Vetchinnikova, A. Konina, Nitin Williams, Nina Mikusová, Anna Mauranen
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引用次数: 1

Abstract

Abstract There have been some suggestions in linguistics and cognitive science that humans process continuous speech by routinely chunking it up into smaller units. The nature of the process is open to debate, which is complicated by the apparent existence of two entirely different chunking processes, both of which seem to be warranted by the limitations of working memory. To overcome them, humans seem to both combine items into larger units for future retrieval (usage-based chunking), and partition incoming streams into temporal groups (perceptual chunking). To determine linguistic properties and cognitive constraints of perceptual chunking, most previous research has employed short-constructed stimuli modeled on written language. In contrast, we presented linguistically naïve listeners with excerpts of natural speech from corpora and collected their intuitive perceptions of chunk boundaries. We then used mixed-effects logistic regression models to find out to what extent pauses, prosody, syntax, chunk duration, and surprisal predict chunk boundary perception. The results showed that all cues were important, suggesting cue degeneracy, but with substantial variation across listeners and speech excerpts. Chunk duration had a strong effect, supporting the cognitive constraint hypothesis. The direction of the surprisal effect supported the distinction between perceptual and usage-based chunking.
实时分组语音:语言预测因素和认知约束
摘要在语言学和认知科学中,有一些建议认为,人类通过将连续语音分成更小的单元来处理连续语音。这一过程的性质还有待商榷,因为明显存在两个完全不同的分块过程,这两个过程似乎都受到工作记忆的限制。为了克服这些问题,人类似乎既将项目组合成更大的单元以供未来检索(基于使用的分块),又将传入流划分为时间组(感知分块)。为了确定感知组块的语言特性和认知约束,以前的大多数研究都采用了以书面语言为模型的短结构刺激。相比之下,我们向语言天真的听众展示了语料库中的自然语音摘录,并收集了他们对区块边界的直观感知。然后,我们使用混合效应逻辑回归模型来找出停顿、韵律、语法、块持续时间和意外预测块边界感知的程度。结果表明,所有线索都是重要的,这表明线索退化,但在听众和演讲节选之间存在显著差异。Chunk持续时间有很强的影响,支持认知约束假说。奇怪效应的方向支持了感知和基于使用的组块之间的区别。
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来源期刊
CiteScore
4.20
自引率
0.00%
发文量
34
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