![]() Speech analysis requires the standardization of speech acquisition to produce consistent results. They argued that it has the advantage of being used stably for automatic depressive detection in clinical settings. Stasak et al observed that, unlike the spontaneous mode, text-dependent affective read speech provides a more accurate ground truth for speech analysis because of speech and affective constraints. Therefore, using it for depression detection can provide a more precise performance even under limited conditions. Text-dependent read speech helps to reduce acoustic variability, as reading language content can be designed to express behavior in a controlled manner, such as the same length and content. Therefore, in this study, we explore how depression detection can benefit from deep learning. These approaches have greatly improved performance because they can automatically learn effective hierarchical representation of speech without human intervention. More recently, deep learning techniques have achieved high success in audio and video recognition tasks, and many studies have reported that deep learning approaches in depression detection have significantly improved performance compared with conventional approaches that use partial representation. Demonstrating the reliable acoustic features of ADD remains an open research challenge. However, handcrafted acoustic features require considerable effort and time, and because the extracted features depend on the researcher’s domain knowledge, some useful information related to depression may be lost. These studies have suggested that acoustic features are closely related to depression. ![]() Among speech-based methods, previous studies have focused more on using handcrafted acoustic features, such as prosody, formant, and cepstral features, and then classifying patterns using ML algorithms, such as support vector machine (SVM), logistic regression, and random forest (RF). ![]() Moreover, it requires significantly less bandwidth and lower processing power, thus making it a simple and computationally inexpensive implementation of depression detection.Īutomatic depression detection (ADD) has gained popularity with the advent of publicly available data sets and the power of ML techniques to learn complex patterns. Among these, speech has proven to be a reliable biomarker for depression assessment and is popular because of its accessibility and availability compared with other behavioral signals, making it ideal data for depression screening. Previous studies have explored a spectrum of behavioral signal approaches, such as speech, text, facial expressions, and body movements, to develop depression assessment. These approaches may make it easier for nonspecialists to effectively identify symptoms in patients with depression and accordingly direct them toward appropriate treatment or management. Ī promising approach to address the abovementioned problems is to identify depression markers and advanced machine learning (ML) techniques using real-world accessible sensors (eg, wearables, cameras, and phones). There is an urgent need to develop a method for reliable automatic diagnosis and timely screening of depression to facilitate remote assessments and more precise treatment with personalization. In addition, it can be difficult to access trained clinicians in a timely manner, and the diagnosis process and quality are inconsistent for patients in need of professional assistance. When diagnosed correctly, depression is a treatable disorder, and its symptoms can be relieved however, an accurate diagnosis of major depression is difficult because it is a biologically and clinically heterogeneous entity. When left untreated, it can affect the quality of life, lower work productivity, and lead to suicide. It leads to a variety of negative health outcomes in individuals. Depression is a serious psychiatric illness affecting >300 million people worldwide.
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