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利用反向关联揭示微笑言语的心理表象。

Uncovering mental representations of smiled speech using reverse correlation.

机构信息

STMS (Sciences et Technologies de la Musique et du Son) Lab (Ircam/CNRS/UPMC), 1 place Igor Stravinsky, Paris, France

出版信息

J Acoust Soc Am. 2018 Jan;143(1):EL19. doi: 10.1121/1.5020989.

Abstract

Which spectral cues underlie the perceptual processing of smiles in speech? Here, the question was addressed using reverse-correlation in the case of the isolated vowel [a]. Listeners were presented with hundreds of pairs of utterances with randomly manipulated spectral characteristics and were asked to indicate, in each pair, which was the most smiling. The analyses revealed that they relied on robust spectral representations that specifically encoded vowel's formants. These findings demonstrate the causal role played by formants in the perception of smile. Overall, this paper suggests a general method to estimate the spectral bases of high-level (e.g., emotional/social/paralinguistic) speech representations.

摘要

哪些频谱线索是言语中微笑感知处理的基础?在这种情况下,使用元音[a]的反相关方法来解决这个问题。研究人员向听众展示了数百对语音样本,这些样本的频谱特征是随机操纵的,并要求他们在每一对中指出哪个更具微笑感。分析表明,听众依赖于特定编码元音共振峰的稳健频谱表示。这些发现证明了共振峰在微笑感知中的因果作用。总的来说,本文提出了一种估计高水平(如情感/社交/副语言)言语表示频谱基础的通用方法。

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