fNIRS experimental study on the impact of AI-synthesized familiar voices on brain neural responses - Weijia Zhang; Jiaju Li; Luyang Ji; Xue Cheng; Dongqin Sun; Yanrong Jiang; Feiyu Chen; Yiduo Zhou; Calvin Choi; Hao Cheng; Shaomin Cai (Scientific Reports)
Abstract With the advancement of artificial intelligence (AI) speech synthesis technology, its application in personalized voice services and its potential role in emotional comfort have become research focal points. This study aims to explore the impact of AI-synthesized familiar and unfamiliar voices on neural responses in the brain. We utilized the GPT-SoVITS project to synthesize three types of voices: a female voice, a sweet female voice, and a maternal voice, all reading the same text. Using functional near-infrared spectroscopy (fNIRS), we monitored the changes in blood oxygen levels in the prefrontal cortex and temporal cortex of participants during listening, assessing brain activation. The experimental results showed that the AI-synthesized maternal voice significantly activated the participants’ prefrontal and temporal cortices. Combined with participants’ feedback, the activation of these areas may reflect multidimensional features of voice familiarity processing, including emotion, memory, and cognitive function. This finding reveals the potential applications of AI voice technology in enhancing mental health and user experience.