The literature suggests that clinical depression is a major public health problem. Spanish- speakers in the United States are at significant risk for depression and in need of culturally-responsive mental health services. Conventional self-report depression assessment methods display limited predictive power. Fortunately, computer-assisted assessment methods offer alternatives to overcome the psychometric and cultural limitations of self-report measures. Most importantly, computerized speech recognition promises to enhance the early and accurate detection of depressed mood and symptoms. The author developed, tested and evaluated several bilingual computerized speech recognition (voice-interactive) depression screening programs that verbally interviewed English and Spanish speakers using the Center for Epidemiological Studies – Depression scale (CES-D). The studies provided evidence that the bilingual voice-interactive speech recognition applications were generally feasible to administer, reliable, valid, and equivalent (means and variabilities) to standard interview (face-to-face and paper-and-pencil) methods. The English and Spanish-speaking samples positively rated the automated interviews. The findings suggested that the applications were culturally and linguistically viable tools for screening depression. The potential of the analysis of speech behavior and voice characteristics for accurately detecting depression among Spanish-speakers is discussed.