arXiv:2603.10371v2 Announce Type: replace-cross Abstract: Speech tokenizers are essential for connecting speech to large language models (LLMs) in multimodal systems. Speech tokenizers are expected to preserve both semantic and acoustic information for downstream understanding and generation tasks. However, emerging evidence suggests that the term "semantic" in speech processing does not align with linguistic lexical-semantic, leading to a mismatch between speech and text modality. In this paper, we systematically analyze the information encoded by several widely used speech tokenizers, evalua
The rapid advancement and integration of large language models into multimodal AI systems necessitate a deeper understanding of how speech is processed and integrated, driving current research into speech tokenization.
This research highlights a fundamental mismatch between how 'semantic' information is handled in speech processing versus linguistic lexical-semantic meaning, which is critical for the effective development of future AI agents and multimodal systems.
The understanding of how speech tokenizers actually encode information will shift, leading to improved architectures that better align speech and text modalities for more robust AI applications.
- · AI developers
- · Multimodal AI systems
- · Natural Language Processing researchers
- · Inefficient speech tokenizer architectures
Improved performance and accuracy in speech-to-text and speech-to-semantic tasks within multimodal AI.
Faster development of sophisticated AI agents capable of more nuanced understanding and generation of human language.
Enhanced human-computer interaction and the acceleration of AI integration into areas requiring deep linguistic comprehension.
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Read at arXiv cs.CL