Xu Tianze, Jiang Xiaoming, Zhang Peng, Wang Anni
Institute of Linguistics, Shanghai International Studies University, Shanghai, 201620, China.
Key Laboratory of Language Science and Multilingual Artificial Intelligence, Shanghai International Studies University, Shanghai, 201620, China.
Behav Res Methods. 2025 Feb 3;57(3):86. doi: 10.3758/s13428-025-02608-3.
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.
现有的语音辨别标准化测试主要基于印欧语系语言，尤其是英语。然而，语音识别感知会受到语言熟悉度的影响，听众通常在母语环境下的表现优于外语环境。为了更准确、全面地评估语音辨别能力，开发针对应试者母语的测试至关重要。为此，我们开发了“思素语音匹配测试”（SVMT），这是一款专门为说普通话的人设计的开创性工具。SVMT旨在模拟现实世界中的交流，因为它既包含伪词刺激也包含伪句刺激，涵盖了将相同语音归类为同一类的能力以及将不同语音归类为不同类的能力。基于经过神经验证的语音空间模型和项目反应理论构建，SVMT确保了高可靠性、有效性、适当的难度和强大的区分能力，同时保持约10分钟的简洁测试时长。因此，通过考虑语言母语的影响，SVMT补充了基于其他语言音系的现有语音测试，以便为说普通话的人提供更准确的语音辨别能力评估。未来的研究可以使用SVMT来加深我们对人类语音识别感知背后机制的理解，特别是在特殊人群中，并研究语音识别与其他认知过程之间的关系。
