Natural speech statistics shift phoneme categorization

Natural speech statistics shift phoneme categorization
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自然语音统计改变音素分类

DOI:
10.3758/s13414-018-01659-3
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发表时间:
2019
期刊:
Attention, Perception, & Psychophysics
影响因子:
--
通讯作者:
Ashley A. Assgari
Ashley A. Assgari
中科院分区:
--
文献类型:
--
作者:
Christian E. Stilp;Ashley A. Assgari

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所有的感知都发生在背景中。给定语音的识别受周围声音的声学特性的影响。当较早(上下文)声音的频谱组成(例如,在较低的第一共振峰[F1]频率处的更多能量)不同于后面(目标)声音的能量(例如,具有中间F1的元音),听觉系统放大这种差异,使目标分类偏向(例如,更高的F1/F2/F3)。历史上,这些研究使用滤波器来迫使上下文声音具有所需的频谱组成。这种方法对于语音的自然信号统计是不可知的(固有的频谱组成,而没有任何额外的操作)。人们认为听觉系统对这些刺激统计数据是协调一致的,但这一点尚未得到验证。在这里,元音分类后测量未过滤(已经拥有所需的频谱组成)或过滤的句子(匹配未过滤的句子的频谱特性)。元音分类在这两种情况下是有偏见的,随着上下文句子中的谱一致性增加,偏见更大。这证实了对自然信号统计的敏感性,将语音感知中的频谱上下文效应扩展到更自然的收听条件。重要的是,分类偏差较小,更可变的未经过滤的句子,提出了重要的问题,如何忠实地使用过滤的上下文模型日常言语感知实验。
All perception takes place in context. Recognition of a given speech sound is influenced by the acoustic properties of surrounding sounds. When the spectral composition of earlier (context) sounds (e.g., more energy at lower first formant [F1] frequencies) differs from that of a later (target) sound (e.g., vowel with intermediate F1), the auditory system magnifies this difference, biasing target categorization (e.g., towards higher-F1 /ɛ/). Historically, these studies used filters to force context sounds to possess desired spectral compositions. This approach is agnostic to the natural signal statistics of speech (inherent spectral compositions without any additional manipulations). The auditory system is thought to be attuned to such stimulus statistics, but this has gone untested. Here, vowel categorization was measured following unfiltered (already possessing the desired spectral composition) or filtered sentences (to match spectral characteristics of unfiltered sentences). Vowel categorization was biased in both cases, with larger biases as the spectral prominences in context sentences increased. This confirms sensitivity to natural signal statistics, extending spectral context effects in speech perception to more naturalistic listening conditions. Importantly, categorization biases were smaller and more variable following unfiltered sentences, raising important questions about how faithfully experiments using filtered contexts model everyday speech perception.
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