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Assessing susceptibility to distraction along the vocal processing hierarchy

机译:评估沿着声音处理等级分散的敏感性

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Recent models of voice perception propose a hierarchy of steps leading from a more general, low-level acoustic analysis of the voice signal to a voice-specific, higher-level analysis. We aimed to engage two of these stages: first, a more general detection task in which voices had to be identified amid environmental sounds, and, second, a more voice-specific task requiring a same/different decision about unfamiliar speaker pairs (Bangor Voice Matching Test [BVMT]). We explored how vulnerable voice recognition is to interfering distractor voices, and whether performance on the aforementioned tasks could predict resistance against such interference. In addition, we manipulated the similarity of distractor voices to explore the impact of distractor similarity on recognition accuracy. We found moderate correlations between voice detection ability and resistance to distraction (r=.44), and BVMT and resistance to distraction (r=.57). A hierarchical regression revealed both tasks as significant predictors of the ability to tolerate distractors (R-2=.36). The first stage of the regression (BVMT as sole predictor) already explained 32% of the variance. Descriptively, the higher-level BVMT was a better predictor (beta=.47) than the more general detection task (beta=.25), although further analysis revealed no significant difference between both beta weights. Furthermore, distractor similarity did not affect performance on the distractor task. Overall, our findings suggest the possibility to target specific stages of the voice perception process. This could help explore different stages of voice perception and their contributions to specific auditory abilities, possibly also in forensic and clinical settings.
机译:最近的语音感知模型提出了一种步骤,这些步骤从语音信号更一般,低级声学分析到语音信号到语音,更高级别的分析。我们的目标是从事其中两个阶段:首先,更一般的检测任务,在环境声音中必须识别声音,而且,第二个是一个更具语音的任务,需要对不熟悉的扬声器对的相同/不同的决定(Bangor语音)匹配测试[BVMT])。我们探讨了易受伤害的语音识别是如何干扰分散的声音,以及上述任务的性能是否可以预测对这种干扰的抵抗力。此外,我们还操纵了分散的声音的相似性,探讨了令人信服的相似性对识别准确性的影响。我们发现语音检测能力与抗度抵抗(r = .44)之间的中等相关性,以及BVMT和耐受分度(R = .57)。分层回归揭示了耐受分类剂的能力的重要预测因子(R-2 = .36)。回归的第一阶段(BVMT作为唯一的预测因子)已经解释了32%的方差。描述性地,更高级别的BVMT是更好的预测因子(Beta = .47),但是比较一般的检测任务(Beta = .25),尽管进一步的分析显示β重量之间没有显着差异。此外,分散人的相似性并不影响令人厌倦的任务的性能。总体而言,我们的调查结果表明了对语音感知过程的特定阶段的可能性。这有助于探索语音感知的不同阶段以及对特定听觉能力的贡献,也可能在法医和临床环境中。

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