The effects of high variability training on voice identity learning
The effects of high variability training on voice identity learning
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DOI：
10.1016/j.cognition.2019.104026
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发表时间：
2019-12-01
期刊：
影响因子：
3.4
通讯作者：
McGettigan, Carolyn
中科院分区：
文献类型：
--
作者：
Lavan, Nadine;Knight, Sarah;McGettigan, Carolyn
高可变性训练已被证明有利于学习新的面孔身份。在三个实验中，我们研究了这是否也适用于语音身份学习。在实验1a中，我们将高变异性训练集（包括从许多不同的录音会话中提取的刺激，说话环境和说话风格）与低变异性刺激集（仅包括从一个录音会话中提取的单一说话风格（阅读语音））进行了对比（参见里奇和伯顿，2017年的面孔）。使用阅读句子（即测试材料与低变异性训练刺激完全重叠）对听众进行了旧/新识别任务的测试，我们发现了高变异性的缺点。在实验1b中，以类似的方式训练听者，然而，现在训练集和测试刺激之间的说话风格或记录会话没有重叠。在这里，我们发现了高可变性的优势。在实验2中，变异性操纵的独特项目的数量，而不是独特的说话风格的数量。在这里，我们将实验1a中使用的高变异性训练集与低变异性训练集进行了对比，低变异性训练集包括相同的风格广度，但独特的项目较少;相反，单个项目被重复（参见Murphy，Ipser，Gaigg和Cook，2015年的面孔）。我们发现只有微弱的证据，高变异性的优势，这可以解释为刺激特异性的影响。我们建议，高变异性的优势可能是特别明显的，当听众需要概括训练的刺激不同的声音，以前闻所未闻的刺激。我们讨论了这些发现的背景下，被认为是支持高变异性训练的优势的机制。
High variability training has been shown to benefit the learning of new face identities. In three experiments, we investigated whether this is also the case for voice identity learning. In Experiment 1a, we contrasted high variability training sets - which included stimuli extracted from a number of different recording sessions, speaking environments and speaking styles - with low variability stimulus sets that only included a single speaking style (read speech) extracted from one recording session (see Ritchie & Burton, 2017 for faces). Listeners were tested on an old/new recognition task using read sentences (i.e. test materials fully overlapped with the low variability training stimuli) and we found a high variability disadvantage. In Experiment 1b, listeners were trained in a similar way, however, now there was no overlap in speaking style or recording session between training sets and test stimuli. Here, we found a high variability advantage. In Experiment 2, variability was manipulated in terms of the number of unique items as opposed to number of unique speaking styles. Here, we contrasted the high variability training sets used in Experiment 1a with low variability training sets that included the same breadth of styles, but fewer unique items; instead, individual items were repeated (see Murphy, Ipser, Gaigg, & Cook, 2015 for faces). We found only weak evidence for a high variability advantage, which could be explained by stimulus-specific effects. We propose that high variability advantages may be particularly pronounced when listeners are required to generalise from trained stimuli to different-sounding, previously unheard stimuli. We discuss these findings in the context of mechanisms thought to underpin advantages for high variability training.