Cross-cultural design for voice recognition systems (VRS) is evolving at a high pace since we are moving towards a digital transformation world. VRS enables devices to understand and interpret an individual’s speech. Fundamentally, it enables machines to listen and understand what an individual is saying by analyzing the distinctive patterns of their voice. Although there has been a massive evolution of VRS performance over the years, these technologies must do an in-depth consideration of the ethical and cultural biases to be able to address the challenges related to diversity, inclusivity, usability, and acceptance. The challenge is that voice recognition technologies omit a large demographic of users because VRS can only acknowledge certain accents, they have been trained to understand. An accent can be defined as a distinctive mode of pronunciation of a language, notably one associated with a specific nation, culture, or social class [1]. Recent studies on VRS technologies highlighted that widely used VRS are less efficient on the voices and accents of minority cultural groups such as Indians or French Africans because artificial intelligence (AI) and machine learning (ML) algorithms are not widely trained to recognize these specific accents [2, 3]. In a study conducted by [4, 5], the authors outlined that Siri, Alexa and other VRS occasionally have trouble with the accents of users from several overlooked cultural groups. This research study addresses voice recognition biases related to cultural differences while investigating solutions for debiasing voice recognition systems. Findings of this study indicated that cultural implications of VRS guidelines could potentially influence users’ behavior and usability and strengthen the effectiveness of cultural globalization. A set of guidelines was proposed to researchers and developers for future considerations to design a more inclusive, usable, and adaptable, VRS technologies, and thus, accepted by users from diverse backgrounds.