Title:
AI & The Sound of Mental Health: Remixed Feelings
Abstract:
Mental health has a sound, and AI is beginning to hear and mix it.
In the voice and language, we find traces of affect, depression, and recovery that can open
new pathways for scalable and personalised care. Starting there, we will explore how speech-based
digital psychology is expanding from diagnosis toward intervention: from vocal and linguistic
biomarkers of mental well-being to large language models that support rather than simply assess,
reaching to generative music systems that enable closed-loop personalised emotional regulation. This
convergence of speech AI, interventive language technology, and generative audio suggests a future
in which intelligent systems can listen, understand, and respond in psychologically meaningful ways.
Realising that future, however, requires more than technical performance. It demands beyond clinical
relevance reliability, safety, explainability, and trust. I will discuss the promise, route, and
responsibility of building AI that does not merely analyse the mind, but helps care and deejay for it.