What information is necessary for speech categorization? Harnessing variability in the speech signal by integrating cues computed relative to expectations.
What information is necessary for speech categorization? Harnessing variability in the speech signal by integrating cues computed relative to expectations.
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DOI：
10.1037/a0022325
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发表时间：
2011-04
影响因子：
5.4
通讯作者：
Jongman, Allard
中科院分区：
文献类型：
--
作者：
McMurray, Bob;Jongman, Allard
大多数范畴化理论强调如何将连续的知觉信息映射到范畴。然而，同样重要的是模型的信息假设，即从属于该映射的信息类型。这在语音感知中是至关重要的，因为在语音感知中，信号是可变的和上下文相关的。本研究评估了几种语音分类模型的信息性假设，特别是作为分类基础的线索数量，以及这些线索是否真实地代表了输入或经过了补偿。我们收集了一个2880个摩擦发音的语料库，横跨许多说话者和元音语境，并为每个语境测量了24个线索。在8AFC音素分类任务中，也向听者呈现了一个子集。然后，我们训练了一个基于Logistic回归的通用分类模型来从线索值中对摩擦进行分类，并对训练集中的信息进行了处理，以对比1)基于少量不变线索的模型，2)使用所有线索而不补偿的模型，以及3)线索对语境因素进行补偿的模型。补偿是通过计算相对于预期的暗示(C-CURE)来建模的，这是一种新的补偿方法，它保留了信号中的细粒度细节。只有补偿模型达到了与听者相似的准确度，并表现出与语境相同的效果。因此，当有足够的信息可用并且采用像C-CURE这样的补偿方案时，即使是简单的分类度量也可以克服语音中的可变性。
Most theories of categorization emphasize how continuous perceptual information is mapped to categories. However, equally important is the informational assumptions of a model, the type of information subserving this mapping. This is crucial in speech perception where the signal is variable and context-dependent. This study assessed the informational assumptions of several models of speech categorization, in particular, the number of cues that are the basis of categorization and whether these cues represent the input veridically or have undergone compensation. We collected a corpus of 2880 fricative productions spanning many talker- and vowel-contexts and measured 24 cues for each. A subset was also presented to listeners in an 8AFC phoneme categorization task. We then trained a common classification model based on logistic regression to categorize the fricative from the cue values, and manipulated the information in the training set to contrast 1) models based on a small number of invariant cues; 2) models using all cues without compensation, and 3) models in which cues underwent compensation for contextual factors. Compensation was modeled by Computing Cues Relative to Expectations (C-CuRE), a new approach to compensation that preserves fine-grained detail in the signal. Only the compensation model achieved a similar accuracy to listeners, and showed the same effects of context. Thus, even simple categorization metrics can overcome the variability in speech when sufficient information is available and compensation schemes like C-CuRE are employed.
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DOI：
10.3758/pp.70.3.403
发表时间：
2008-04-01
期刊：
PERCEPTION & PSYCHOPHYSICS
影响因子：
--
作者：
Connine, Cynthia M.;Ranbom, Larissa J.;Patterson, David J.
通讯作者：
Patterson, David J.
影响因子：
5.4
作者：
ASHBY, FG;PERRIN, NA
通讯作者：
PERRIN, NA
影响因子：
2.4
作者：
CRYSTAL, TH;HOUSE, AS
通讯作者：
HOUSE, AS
影响因子：
1.9
作者：
Cole, Jennifer;Linebaugh, Gary;McMurray, Bob
通讯作者：
McMurray, Bob
影响因子：
5.4
作者：
Feldman, Naomi H.;Griffiths, Thomas L.;Morgan, James L.
通讯作者：
Morgan, James L.