In this dissertation, we aim to establish a language-universal framework for ASR that overcomes language-specific limitations by leveraging universal speech attributes, such as manner and place of articulation. These attributes, which remain consistent across all languages, serve as a foundation for building multilingual models capable of performing effectively across diverse linguistic settings. Our approach seeks to address the lack of knowledge sharing across languages due to linguistic distance, where traditional language-dependent tokens are insufficient. By utilizing a compact set of language-universal speech attributes, we aim to bridge the performance gap for low-resource and unseen languages, enhancing the adaptability and scalability of ASR systems.