UniMoE-Audio: Unified Speech and Music Generation with Dynamic-Capacity Mixture-of-Experts

ACL ARR 2026 January Submission10156 Authors

Keywords: Multimodal Large Language Model, Audio Generation, Mix-of-Experts

Abstract:

Recent advances in unified multimodal models indicate a clear trend towards comprehensive content generation. However, the auditory domain remains a significant challenge, with music and speech often developed in isolation, hindering progress towards universal audio synthesis. This separation stems from inherent task conflicts between semantic speech and structural music modeling, and severe data imbalances, which impede the development of a truly unified model.
To address these challenges, we propose **UniMoE-Audio**, a unified speech and music generation model built upon a novel **D**ynamic-**C**apacity **M**ix-**o**f-**E**xperts (DCMoE) framework. Architecturally, UniMoE-Audio extends the conventional MoE paradigm by introducing a Top-$P$ routing strategy for adaptive capacity allocation.
To tackle data imbalance, we introduce a three-stage training curriculum: 1) Independent Specialist Training leverages original datasets to instill domain-specific knowledge into each specialists without interference; 2) MoE Integration and Warmup incorporates these specialists into the UniMoE-Audio architecture, warming up the gate module and shared expert using a subset of balanced dataset; and 3) Synergistic Joint Training trains the entire model end-to-end on the fully balanced dataset, fostering enhanced cross-domain synergy.
Extensive experiments show that UniMoE-Audio not only achieves state-of-the-art performance on major speech and music generation benchmarks, but also demonstrates superior synergistic learning, mitigating the performance degradation typically seen in naive joint training. Our findings highlight the substantial potential of specialized MoE architecture and curated training strategies in advancing universal audio generation.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: multimodal applications

Contribution Types: NLP engineering experiment, Publicly available software and/or pre-trained models

Languages Studied: English, Chinese

Submission Number: 10156