  Mild Cognitive Impairment (MCI) is a medical condition characterized by
noticeable declines in memory and cognitive abilities, potentially affecting
individual's daily activities. In this paper, we introduce CogniVoice, a novel
multilingual and multimodal framework to detect MCI and estimate Mini-Mental
State Examination (MMSE) scores by analyzing speech data and its textual
transcriptions. The key component of CogniVoice is an ensemble multimodal and
multilingual network based on ``Product of Experts'' that mitigates reliance on
shortcut solutions. Using a comprehensive dataset containing both English and
Chinese languages from TAUKADIAL challenge, CogniVoice outperforms the best
performing baseline model on MCI classification and MMSE regression tasks by
2.8 and 4.1 points in F1 and RMSE respectively, and can effectively reduce the
performance gap across different language groups by 0.7 points in F1.
