Participants will be recruited to complete self reported surveys normally used as standards of care for screening and monitoring depression and anxiety symptom severity, provide a voice sample composed of an answer to open ended questions and then be assessed by a mental health professional using structured and clinically validated assessment tools for depression and anxiety. Their voice will be analyzed by machine learning models that predict the severity of depression and anxiety symptoms. The models' performance will be compared to the clinician assessments and how that correlation compares to a similar comparison between the clinician assessments with the self reported surveys. It is hypothesized that the performance of the machine learning models in assessing the severity of depression and anxiety symptoms is no worse than the self reported surveys when both are compared to clinician assessments. It is also hypothesized that presence or absence of the diagnoses of Major Depressive Disorder and Generalized Anxiety Disorder can be predicted better than chance by the analysis of the participant's voice sample using machine learning models.

Inclusion Criteria:

• Native speaker or conversant in English
• Access to smartphone or computer with microphone
• Provision of esigned and dated informed consent form
• Willingness to adhere to the study protocol
• To participate in the subsequent clinical interview portion of this study in addition to the above inclusion criteria, a participant must provide an evaluable and qualified voice sample.

Exclusion Criteria:

• Speech impairments or other conditions that impact their ability to speak clearly
• Under the influence of recreational drugs or alcohol
• Ill or experiencing heavy allergies or temporary conditions affecting respiration, voice, or speaking.

~540 individuals aged 18 and above, of any gender, race, culture, ethnicity, sexual orientation or educational level residing anywhere in the United States.