arXiv:2609.09719v1 Announce Type: new
Abstract: Text-aligned speech tokenization methods have emerged to better align speech tokens with LLM token spaces, enabling more effective utilization of pretr...
By Kang-wook Kim, Jinyoung Park, Jinsoo Kim, Sehun Lee, Sang Hoon Woo, Gunhee Kim
Text-aligned speech tokenization methods have emerged to better align speech tokens with LLM token spaces, enabling more effective utilization of pretrained LLMs. However, they rely on offline automat...
arXiv:2609.13045v1 Announce Type: new
Abstract: Speech-to-speech translation (S2ST) has advanced significantly with speech LLMs, offering the potential for joint optimization and preserving non-lingu...
By Hayato Futami, Hassan Shahmohammadi, Tushar Dhyani, Alkis Koudounas, Rapha\"el Lafargue, Yosuke Kashiwagi, Quentin Jodelet, Emiru Tsunoo
TokenMapper is a framework that enables direct translation between different speech tokenizers, allowing heterogeneous speech models to communicate without converting tokens to waveform audio. It handles mismatched token spaces, including single and multi-codebook representations, while maintaining a shared effective token rate. Experiments on GLM-4-Voice, MiMi, and DualCodec show that TokenMapper achieves word error rates close to native reconstructions, comparable human MOS scores, and significantly reduces latency compared to waveform bridging.
By Tal Kozakov, Tal Rosenwein, Eliya Nachmani
arXiv:2606. 09234v1 Announce Type: cross Abstract: Recent state-of-the-art (SOTA) text-to-speech (TTS) systems typically adopt a cascaded pipeline consisting of a speech tokenizer, an autoregressive large language model (LLM), and a diffusion based flow-matching (FM) model, with these components trained independently.
By Changfeng Gao, Yong Ren, Jun Yuan, Ye Bai, Zhao You, ShiDong Shang
The paper introduces Hybrid Search, a method that refines warm-initialized large language model (LLM) based automatic speech recognition (ASR) systems by exploiting interactions between ASR hidden states and the base LLM’s hidden states. By identifying tokens with high semantic dependence and selectively correcting them, the approach surpasses traditional global LLM‑correction techniques such as rescoring and late fusion. The study demonstrates that even after warm initialization, LLM‑based ASR models can further benefit from their base LLM during inference.
By Chan-Jan Hsu, Jaeyeon Kim, Chao-Han Huck Yang, Shinji Watanabe, Hung-yi Lee, Carlos Busso