Cognition ( IF 2.8 ) Pub Date : 2021-02-15 , DOI: 10.1016/j.cognition.2021.104619
Xin Xie 1 , Andrés Buxó-Lugo 2 , Chigusa Kurumada 1
Speech prosody plays an important role in communication of meaning. The cognitive and computational mechanisms supporting this communication remain to be understood, however. Prosodic cues vary across talkers and speaking conditions, creating ambiguity in the sound-to-meaning mapping. We hypothesize that listeners ameliorate this ambiguity in part by learning talker-specific statistics of prosodic cues. To test this hypothesis, we investigate the production and recognition of question vs. statement prosody in American English. Experiment 1 elicits productions of questions and statements from 65 talkers to examine the distributional statistics characterizing within- and cross-talker variability in these productions. We use Bayesian ideal observer models to assess the predicted consequences of cross-talker variability on listeners' recognition of prosody. We find that learning of talker-specific distributional statistics is predicted to facilitate recognition, above and beyond what can be achieved via commonly assumed normalizations of prosodic cues. Experiment 2 tests this prediction in a comprehension experiment. We expose different groups of listeners to different prosodic input statistics and assess listeners' recognition of questions and statements both prior to, and following, exposure. Prior to exposure, ideal observer-derived predictions based on Experiment 1 provide a good qualitative fit against listeners' recognition of prosodic contours in Experiment 2. Following exposure, listeners shift the categorization boundary between questions and statements in ways consistent with learning of talker-specific statistics.
中文翻译:
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通过国际语音韵律中的结构变异对意义进行编码和解码
言语韵律在意义传达中起着重要作用。但是,支持这种交流的认知和计算机制仍有待理解。韵律提示在讲话者和讲话条件之间会有所不同,从而在声音到意思的映射中产生歧义。我们假设听众可以通过学习特定于说话者的韵律提示统计数据来缓解这种歧义。为了检验该假设,我们研究了美式英语中问题与陈述韵律的产生和识别。实验1产生了65位谈话者的问题和陈述,以检验表征这些谈话内容内和谈话者变异性的分布统计。我们使用贝叶斯理想观察者模型来评估串扰者变异性对听众的预期后果 承认韵律。我们发现,对讲话者特定的分布统计数据的学习预计会促进识别,这超出了通常通过韵律提示的归一化可以实现的范围。实验2在理解实验中测试了此预测。我们向不同的听众群体提供不同的韵律输入统计数据,并评估听众在暴露之前和之后对问题和陈述的认识。暴露之前,基于实验1的理想的观察者派生预测提供了很好的定性拟合,从而反对听众在实验2中识别韵律轮廓。暴露之后,听众以与学习特定于讲话者的方式一致的方式改变问题和陈述之间的分类边界统计数据。我们发现,对讲话者特定的分布统计数据的学习预计会促进识别,这超出了通常通过韵律提示的归一化可以实现的范围。实验2在理解实验中测试了此预测。我们向不同的听众群体提供不同的韵律输入统计数据,并评估听众在暴露之前和之后对问题和陈述的认识。暴露之前,基于实验1的理想的观察者派生预测提供了很好的定性拟合,从而反对听众在实验2中识别韵律轮廓。暴露之后,听众以与学习特定于讲话者的方式一致的方式改变问题和陈述之间的分类边界统计数据。我们发现,对讲话者特定的分布统计数据的学习预计会促进识别,这超出了通常通过韵律提示的归一化可以实现的范围。实验2在理解实验中测试了此预测。我们向不同的听众群体提供不同的韵律输入统计数据,并评估听众在暴露之前和之后对问题和陈述的认识。暴露之前,基于实验1的理想的观察者派生预测提供了很好的定性拟合,从而反对听众在实验2中识别韵律轮廓。暴露之后,听众以与学习特定于讲话者的方式一致的方式改变问题和陈述之间的分类边界统计数据。