Despite continuing medical advances, the rate of newborn morbidity and mortality globally remains high, with over 6 million casualties every year. The prediction of pathologies affecting newborns based on their cry is thus of significant clinical interest, as it would facilitate the development of accessible, low-cost diagnostic tools\cut{ based on wearables and smartphones}. However, the inadequacy of clinically annotated datasets of infant cries limits progress on this task. This study explores a neural transfer learning approach to developing accurate and robust models for identifying infants that have suffered from perinatal asphyxia. In particular, we explore the hypothesis that representations learned from adult speech could inform and improve performance of models developed on infant speech. Our experiments show that models based on such representation transfer are resilient to different types and degrees of noise, as well as to signal loss in time and frequency domains.

中文翻译：

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基于哭声的围产期窒息诊断的神经迁移学习

尽管医学不断进步，但全球新生儿的发病率和死亡率仍然很高，每年有超过 600 万人伤亡。因此，根据新生儿的哭声预测影响新生儿的病理具有重要的临床意义，因为这将有助于开发可访问的、低成本的诊断工具\cut{基于可穿戴设备和智能手机}。然而，婴儿哭声临床注释数据集的不足限制了这项任务的进展。本研究探索了一种神经转移学习方法，以开发用于识别围产期窒息婴儿的准确而稳健的模型。特别是，我们探索了这样一个假设，即从成人语音中学习的表征可以为婴儿语音开发的模型提供信息并提高其性能。