Speech remains one of the most visible yet overlooked vectors of inclusion and exclusion in contemporary society. While fluency is often equated with credibility and competence, individuals with atypical speech patterns are routinely marginalized. Given the current state of the debate, this article focuses on the structural biases that shape perceptions of atypical speech and are now being encoded into artificial intelligence. Automated speech recognition (ASR) systems and voice interfaces, trained predominantly on standardized speech, routinely fail to recognize or respond to diverse voices, compounding digital exclusion. As AI technologies increasingly mediate access to opportunity, the study calls for inclusive technological design, anti-bias training to minimize the impact of discriminatory algorithmic decisions, and enforceable policy reform that explicitly recognize speech diversity as a matter of equity, not merely accessibility. Drawing on interdisciplinary research, the article advocates for a cultural and institutional shift in how we value voice, urging co-created solutions that elevate the rights, representation, and realities of atypical speakers in the digital age. Ultimately, the article reframes speech inclusion as a matter of equity (not accommodation) and advocates for co-created AI systems that reflect the full spectrum of human voices.

翻译：语音在当代社会中仍是最显著却又最易被忽视的包容与排斥载体之一。尽管流利度常被等同于可信度与能力，具有非典型语音模式的个体却长期处于边缘地位。基于当前讨论现状，本文聚焦于塑造非典型语音认知的结构性偏见——这些偏见正被编码至人工智能系统中。主要基于标准化语音训练的自动语音识别（ASR）系统与语音交互界面，往往无法识别或响应多样化的声音，从而加剧数字排斥。随着人工智能技术日益成为获取机会的中介，本研究呼吁：采用包容性技术设计，通过反偏见训练减少歧视性算法决策的影响，并推行可执行的政策改革——明确将语音多样性视为公平性问题而非单纯的可访问性问题。文章借鉴跨学科研究成果，倡导在语音价值认知层面进行文化与制度转型，敦促通过协同共创的解决方案，提升数字时代中非典型语音使用者的权利、表征与现实处境。最终，本文将语音包容重新定义为公平性问题（而非适应性调整），并倡导构建能够反映人类语音全谱系的协同共创式人工智能系统。