We present JNV (Japanese Nonverbal Vocalizations) corpus, a corpus of Japanese nonverbal vocalizations (NVs) with diverse phrases and emotions. Existing Japanese NV corpora either lack phrase diversity or focus on a small number of emotions, which makes it difficult to analyze the characteristics of Japanese NVs and support downstream tasks like emotion recognition. We first propose a corpus-design method that contains two phases: (1) collecting NVs phrases based on crowd-sourcing; (2) recording NVs by stimulating speakers with emotional scenarios. We then collect 420 audio clips from 4 speakers that cover 6 emotions based on the proposed method. Results of comprehensive objective and subjective experiments demonstrate that (1) the emotions of the collected NVs can be recognized with high accuracy by both human evaluators and statistical models; (2) the collected NVs have a high authenticity comparable to previous corpora of English NVs. Additionally, we analyze the distributions of vowel types in Japanese and conduct feature importance analysis to show discriminative acoustic features between emotion categories in Japanese NVs. We publicate JNV to advance further development in this field.

我们提出了 JNV（日语非语言发声）语料库，这是一个具有不同短语和情感的日语非语言发声（NV）语料库。现有的日语 NV 语料库要么缺乏短语多样性，要么只关注少量情感，这使得分析日语 NV 的特征和支持情感识别等下游任务变得困难。我们首先提出了一种语料库设计方法，该方法包含两个阶段：（1）基于众包收集 NVs 短语；(2)通过情感场景刺激说话者来录制NV。然后，我们根据所提出的方法收集了来自 4 个说话者的 420 个音频片段，涵盖了 6 种情绪。综合客观和主观实验的结果表明：（1）人类评估者和统计模型都可以高精度地识别收集的 NV 的情绪；(2)所收集的NV与以往的英文NV语料库相比具有较高的真实性。此外，我们还分析了日语中元音类型的分布，并进行了特征重要性分析，以显示日语 NV 中情感类别之间的区别性声学特征。我们发布JNV以推动该领域的进一步发展。