In this work we perform a scoping review of the current literature on the detection of throat cancer from speech recordings using machine learning and artificial intelligence. We find 22 papers within this area and discuss their methods and results. We split these papers into two groups - nine performing binary classification, and 13 performing multi-class classification. The papers present a range of methods with neural networks being most commonly implemented. Many features are also extracted from the audio before classification, with the most common bring mel-frequency cepstral coefficients. None of the papers found in this search have associated code repositories and as such are not reproducible. Therefore, we create a publicly available code repository of our own classifiers. We use transfer learning on a multi-class problem, classifying three pathologies and healthy controls. Using this technique we achieve an unweighted average recall of 53.54%, sensitivity of 83.14%, and specificity of 64.00%. We compare our classifiers with the results obtained on the same dataset and find similar results.

翻译：本研究对当前利用机器学习与人工智能从语音录音中检测喉癌的相关文献进行了范围综述。我们检索到该领域的22篇论文，并对其方法与结果进行了讨论。这些论文被分为两组：9篇进行二分类任务，13篇进行多分类任务。这些文献展示了多种方法，其中神经网络的应用最为普遍。在分类前，研究者还从音频中提取了大量特征，其中最常见的是梅尔频率倒谱系数。本次检索发现的所有论文均未提供相关代码库，因此不具备可复现性。为此，我们创建了一个公开可用的分类器代码库。我们针对一个包含三种病理状态和健康对照的多分类问题采用了迁移学习，使用该技术取得了未加权平均召回率53.54%、灵敏度83.14%和特异度64.00%的结果。我们将分类器与基于同一数据集的其他研究结果进行了比较，发现性能基本一致。