Nonverbal vocalizations from non- and minimally speaking individuals who speak fewer than 20 words (mv* individuals) convey important communicative and affective information. While nonverbal vocalizations that occur amidst typical speech and infant vocalizations have been studied extensively in the literature, there is limited prior work on vocalizations by mv* individuals. Our work is among the first studies of the communicative and affective information expressed in nonverbal vocalizations by mv* children and adults. We collected labeled vocalizations in real-world settings with eight mv* communicators, with communicative and affective labels provided in-the-moment by a close family member. Using evaluation strategies suitable for messy, real-world data, we show that nonverbal vocalizations can be classified by function (with 4- and 5-way classifications) with F1 scores above chance for all participants. We analyze labeling and data collection practices for each participating family, and discuss the classification results in the context of our novel real-world data collection protocol. The presented work includes results from the largest classification experiments with nonverbal vocalizations from mv* communicators to date.

中文翻译：

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模拟来自很少说话的人的现实世界情感和交流非语言发声


言语少于 20 个单词的非语言和最低限度语言个体（mv* 个体）的非语言发声传达了重要的交流和情感信息。虽然文献中对典型言语和婴儿发声中发生的非语言发声进行了广泛研究，但之前对 mv* 个体发声的研究有限。我们的工作是对 mv* 儿童和成人非语言发声中表达的交流和情感信息的首批研究之一。我们使用 8 个 mv* 沟通者收集了现实世界环境中带标签的发声，并由一位亲密的家庭成员即时提供沟通和情感标签。使用适合混乱的现实世界数据的评估策略，我们表明非语言发声可以按功能进行分类（4 路和 5 路分类），所有参与者的 F1 分数都高于机会。我们分析每个参与家庭的标签和数据收集实践，并在我们新颖的现实世界数据收集协议的背景下讨论分类结果。所提出的工作包括迄今为止最大的 mv* 传播者非语言发声分类实验的结果。