Do We Need Distinct Representations for Every Speech Token? Unveiling and Exploiting Redundancy in Large Speech Language Models

This project addresses a critical bottleneck in native speech models: the high frame rate of audio tokens (often 50-100Hz) compared to the slow semantic rate of human speech. While the insight—that deeper layers in transformers represent more abstract, redundant concepts—is well-documented in NLP (e.g., Token Merging/ToMe), applying it specifically to the speech modality is a timely but narrow contribution. With 0 stars and 4 forks in 9 days, it is a brand-new research artifact. The defensibility is low because the 'moat' is purely algorithmic insight which, once published, is easily integrated into any LSLM training or inference pipeline. Frontier labs like OpenAI (GPT-4o) and Google (Gemini) are the primary stakeholders for this type of optimization; they are highly likely to have already implemented similar proprietary compression or variable-rate tokenization schemes. The project serves more as a 'recipe' for efficiency rather than a standalone product, making it highly susceptible to absorption by the platforms that host the base models.