Audio large language models (LLMs) have advanced rapidly in general audio understanding, yet consistently underperform on tasks requiring fine-grained acoustic discrimination. We investigate this gap through a systematic probing study, uncovering a phenomenon we term representation reorganization: the LLM’s middle layers construct semantic discriminability at the cost of selectively compressing fine-grained acoustic features such as speaker identity and pitch. Informed by this diagnosis, we propose Overtone, a delayed acoustic fusion architecture that bypasses the identified compression zone. Overtone processes identified audio segments through dual tracks—a semantic track from the LLM’s audio encoder and an acoustic track from a frozen self-supervised speech model—and injects acoustic features via gated cross-attention at post-compression layers. The model learns to autonomously decide when and where re-listening is needed during inference. On MMAU and MMAR, Overtone achieves state-of-the-art results among open-source models at comparable scale, with gains concentrated in speech and music—the domains most reliant on fine-grained acoustic features. Post-training probing confirms recovery of acoustic structure in the reorganized layers.

Text-to-speech (TTS) has seen significant advancements in high-quality, expressive speech synthesis. However, achieving diverse and natural prosody in synthesized speech remains challenging. In this paper, we propose ProsodyFlow, an end-to-end TTS model that integrates large self-supervised speech models and conditional flow matching to model prosodic features effectively. Our approach involves using a speech LLM to extract acoustic features, mapping these features into a prosody latent space, and then employing conditional flow matching to generate prosodic vectors conditioned on the input text. Experiments on the LJSpeech dataset show that ProsodyFlow improves synthesis quality and efficiency compared to existing models, achieving more prosodic and expressive speech synthesizing.