There is a substantial unmet need to diagnose speech-related disorders effectively. Machine
learning (ML), as an area of artificial intelligence (AI), enables researchers, physicians, and …

Efficient detection of depression is a challenging scenario in the field of speech signal
processing. Since the speech signals provide a better diagnosis of depression, a significant …

Introduction Depressive and manic states contribute significantly to the global social burden,
but objective detection tools are still lacking. This study investigates the feasibility of utilizing …

Depression has profound personal, societal, and economic impacts. Leveraging advances
in technology can help identify predictors of depression. In this study, we compared seven …

The aim was to define the association between the severity of depression, prosody, and
voice acoustic features in women suffering from depression and its comparisons with …

Research on speech emotion recognition (SER) is ongoing and has numerous applications
in fields like healthcare, education, and human-computer interaction. The RAVDESS dataset …

Background Both depressive and mania mood state have high prevalence and are
important causes of social burden worldwide, however, there is still no objective indicator for …

[ES] La presente tesis gira en torno al fenómeno de la disforia de voz, entendida desde la
intersección de la identidad de género con el sexo asignado. La disforia de voz hace …

Amongst vast variety of mental health disorders one of the most common is Depression.
Depression is characterized by feeling sad, empty-minded and complete lack of interest. The …

Retracted: Empirical Investigation for Predicting Depression from Different Machine
Learning Based Voice Recognition Techniques Page 1 Retraction Retracted: Empirical …