Out-of-distribution (OOD) detection is concerned with identifying data points that do not belong to the same distribution as the model's training data. For the safe deployment of predictive models in a real-world environment, it is critical to avoid making confident predictions on OOD inputs as it can lead to potentially dangerous consequences. However, OOD detection largely remains an under-explored area in the audio (and speech) domain. This is despite the fact that audio is a central modality for many tasks, such as speaker diarization, automatic speech recognition, and sound event detection. To address this, we propose to leverage feature-space of the model with deep k-nearest neighbors to detect OOD samples. We show that this simple and flexible method effectively detects OOD inputs across a broad category of audio (and speech) datasets. Specifically, it improves the false positive rate (FPR@TPR95) by 17% and the AUROC score by 7% than other prior techniques.

翻译：分布外检测旨在识别与模型训练数据分布不一致的数据点。在真实环境中安全部署预测模型时，必须避免对分布外输入做出高置信度的预测，因为这可能导致潜在的危险后果。然而，分布外检测在音频（及语音）领域仍是一个尚未充分探索的研究方向——尽管音频是说话人分离、自动语音识别和声音事件检测等诸多任务的核心模态。为解决这一问题，本文提出利用模型的深层k最近邻特征空间来检测分布外样本。实验表明，这种简单而灵活的方法能够有效检测涵盖广泛类别的音频（及语音）数据集中的分布外输入。具体而言，相较于现有技术，本方法在TPR95条件下的假阳性率降低了17%，AUROC评分提升了7%。