Daily Papers — 2026-10-13

2 papers on audio, speech, music, and acoustics.

1. Fish Audio Drama 3 Technical Report

Authors: Dawei Han, Shidong Li, Shijia Liao, Jiahua Liu, Songting Liu et al.

Categories: cs.SD, cs.CL

We introduce Fish Audio Drama 3, an open-source speech generation model that turns text into directed performances, built on a 35B-parameter mixture-of-experts backbone that activates 3B parameters per token. It clones voices from multiple references, follows natural-language direction, revises a take when the next turn gives a note, voices multi-character scenes, edits existing speech by infilling, and synthesizes long-form and streaming audio. Multi-turn revision, scene dubbing, editing by infilling, and streaming text input are new in Drama 3, all in one model. Drama 3 pairs the hybrid linear-attention backbone with a new codebook multi-token-prediction audio decoder that predicts the acoustic codebooks of each frame in dedicated layers that attend over the entire sequence. The model is trained with a three-stage pre-training recipe, instruction fine-tuning, and large-batch reinforcement learning whose only reward is a single learned model of human preference. Its inference engine decodes a single stream at 271 frames per second on one H200, against 177 for Fish Audio S2, although Drama 3 has more than seven times as many parameters. The model weights are available on GitHub (https://github.com/fishaudio/fish-speech) and Hugging Face (https://huggingface.co/fishaudio/drama-3), and inference is available through SGLang-Omni (https://github.com/sgl-project/sglang-omni). We highly encourage readers to visit https://fish.audio to direct their own performances.

2. PhonoVoxtLM: Exploring Single-Codebook Phonological Tokens for Speech Understanding and Generation

Authors: Mingrui Liang, Kentaro Onda, Shinji Watanabe

Categories: eess.AS

We investigate how speech token representations can support both understanding and generation within a unified speech language model. We present PhonoVoxtLM, built on the existing VoxtLM framework using phonological tokens designed to retain linguistic content and prosodic information. The existing tokenizer is jointly trained for speech recognition and resynthesis. Its single-codebook units serve as both speech inputs and generation targets, without additional speech-token prediction modules. Experiments show improvements over HuBERT-based baselines across understanding and generation under different training-data scales. Further comparisons with WavLM-based tokens reveal trade-offs between recognition and synthesis, highlighting the importance of evaluating shared representations across both capabilities. We further extend the framework to spoken question answering through fine-tuning with replay. The adapted model improves answering performance over similarly adapted HuBERT baselines while retaining advantages on the original speech tasks. Our findings support single-codebook representations designed for both content and prosody as a practical choice for unified multitask speech modeling.