The approach proposed in this study includes methods specifically dedicated to the detection of allophonic variation in English. This study aims to find an efficient method for automatic evaluation of aspiration in the case of Polish second-language (L2) English speakers' pronunciation when whole words are analyzed instead of particular allophones extracted from words. Sample words including aspirated and unaspirated allophones were prepared by experts in English phonetics and phonology. The datasets created include recordings of words pronounced by nine native English speakers of standard southern British accent and 20 Polish L2 English users. Complete unedited words are treated as input data for feature extraction and classification algorithms such as k-nearest neighbors, naive Bayes method, long-short term memory, and convolutional neural network (CNN). Various signal representations, including low-level audio features, the so-called mid-term and feature trajectory, and spectrograms, are tested in the context of their usability for the detection of aspiration. The results obtained show high potential for an automated evaluation of pronunciation focused on a particular phonological feature (aspiration) when classifiers analyze whole words. Additionally, CNN returns satisfying results for the automated classification of words containing aspirated and unaspirated allophones produced by Polish L2 speakers.

中文翻译：

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使用机器学习评估 L2 英语发音中的愿望问题

本研究中提出的方法包括专门用于检测英语异音变体的方法。本研究旨在寻找一种有效的方法来自动评估波兰语第二语言 (L2) 英语使用者的发音，当分析整个单词而不是从单词中提取特定的异位音时。包括送气和不送气同位异音在内的示例词由英语语音学和音系学专家准备。创建的数据集包括由 9 名母语为标准英国南部口音的英语母语者和 20 名波兰语 L2 英语用户发音的单词录音。完整的未编辑词作为输入数据进行特征提取和分类算法，如k-最近邻、朴素贝叶斯方法、长短期记忆和卷积神经网络 (CNN)。各种信号表示，包括低级音频特征、所谓的中期和特征轨迹以及频谱图，都在其用于检测吸入的可用性的背景下进行了测试。获得的结果表明，当分类器分析整个单词时，对专注于特定语音特征（愿望）的发音进行自动评估的潜力很大。此外，CNN 对包含由波兰语 L2 说话者产生的送气和非送气同位异音的单词的自动分类返回了令人满意的结果。