Existing standardized tests for voice discrimination are based mainly on Indo-European languages, particularly English. However, voice identity perception is influenced by language familiarity, with listeners generally performing better in their native language than in a foreign one. To provide a more accurate and comprehensive assessment of voice discrimination, it is crucial to develop tests tailored to the native language of the test takers. In response, we developed the Sisu Voice Matching Test (SVMT), a pioneering tool designed specifically for Mandarin Chinese speakers. The SVMT was designed to model real-world communication since it includes both pseudo-word and pseudo-sentence stimuli and covers both the ability to categorize identical voices as the same and the ability to categorize distinct voices as different. Built on a neurally validated voice-space model and item response theory, the SVMT ensures high reliability, validity, appropriate difficulty, and strong discriminative power, while maintaining a concise test duration of approximately 10 min. Therefore, by taking into account the effects of language nativeness, the SVMT complements existing voice tests based on other languages' phonologies to provide a more accurate assessment of voice discrimination ability for Mandarin Chinese speakers. Future research can use the SVMT to deepen our understanding of the mechanisms underlying human voice identity perception, especially in special populations, and to examining the relationship between voice identity recognition and other cognitive processes.