Heart failure (HF) is a major global health concern and is increasing in prevalence. It affects the larynx and breathing – thereby the quality of speech. In this article, we propose an approach for the automatic detection of people with HF using the speech signal. The proposed method explores mel-frequency cepstral coefficient (MFCC) features, glottal features, and their combination to distinguish HF from healthy speech. The glottal features were extracted from the voice source signal estimated using glottal inverse filtering. Four machine learning algorithms, namely, support vector machine, Extra Tree, AdaBoost, and feed-forward neural network (FFNN), were trained separately for individual features and their combination. It was observed that the MFCC features yielded higher classification accuracies compared to glottal features. Furthermore, the complementary nature of glottal features was investigated by combining these features with the MFCC features. Our results show that the FFNN classifier trained using a reduced set of glottal + MFCC features achieved the best overall performance in both speaker-dependent and speaker-independent scenarios.

心力衰竭（HF）是全球主要的健康问题，并且患病率正在上升。它会影响喉咙和呼吸，从而影响言语质量。在本文中，我们提出了一种使用语音信号自动检测HF患者的方法。该方法探讨了梅尔频率倒谱系数（MFCC）特征，声门特征及其组合，以区分HF与健康语音。从使用声门逆滤波估计的语音源信号中提取声门特征。分别训练了四种机器学习算法，分别是支持向量机，额外树，AdaBoost和前馈神经网络（FFNN），以针对各个特征及其组合进行训练。观察到，与声门特征相比，MFCC特征产生了更高的分类精度。此外，通过将这些特征与MFCC特征相结合，研究了声门特征的互补性。我们的结果表明，使用减少的声门+ MFCC功能集进行训练的FFNN分类器在与说话者相关和与说话者无关的场景中均获得了最佳的整体性能。