Department of Speech, Hearing and Phonetic Sciences, University College London, United Kingdom; Department of Psychology, Royal Holloway, University of London, United Kingdom.
Department of Speech, Hearing and Phonetic Sciences, University College London, United Kingdom.
Cognition. 2019 Dec;193:104026. doi: 10.1016/j.cognition.2019.104026. Epub 2019 Jul 16.
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.
高变异性训练已被证明有利于新面孔身份的学习。在三个实验中，我们研究了这是否也适用于声音身份学习。在实验 1a 中，我们对比了高变异性训练集——包括从多个不同录音会话、说话环境和说话风格中提取的刺激——与低变异性刺激集，后者仅包括从一个录音会话（见 Ritchie & Burton, 2017 年的面孔研究）中提取的单一说话风格（朗读语音）。听众使用朗读句子（即测试材料与低变异性训练刺激完全重叠）进行新旧识别任务测试，我们发现高变异性有劣势。在实验 1b 中，听众以类似的方式接受训练，但现在训练集和测试刺激之间没有说话风格或录音会话的重叠。在这里，我们发现了高变异性的优势。在实验 2 中，变异性是通过独特项目的数量而不是独特说话风格的数量来操纵的。在这里，我们对比了实验 1a 中使用的高变异性训练集与低变异性训练集，后者包括相同广度的风格，但独特项目较少；相反，单个项目会重复（见 Murphy, Ipser, Gaigg, & Cook, 2015 年的面孔研究）。我们只发现了微弱的高变异性优势证据，这可以用刺激特异性效应来解释。我们提出，当听众需要从训练过的刺激推广到不同声音、以前未听过的刺激时，高变异性优势可能更为明显。我们在支持高变异性训练优势的机制背景下讨论了这些发现。
