Between- and within-person comparisons of speech markers can be leveraged in detecting and monitoring bipolar depression. We demonstrate the feasibility of applying Linguistic Inquiry and Word Count-based psycholinguistic analysis to machine-transcribed and translated speech, supporting the replicability of this approach across languages. Automated multimodal voice analysis can be integrated into digital health platforms, providing a scalable and effective approach for accessing mental health monitoring and care.