Unifying Speech Recognition, Synthesis and Conversion with Autoregressive Transformers

• URL: http://arxiv.org/abs/2601.10770v1
• Date: Thu, 15 Jan 2026 13:47:55 GMT
• Title: Unifying Speech Recognition, Synthesis and Conversion with Autoregressive Transformers
• Authors: Runyuan Cai, Yu Lin, Yiming Wang, Chunlin Fu, Xiaodong Zeng,
• Abstract summary: General-Purpose Audio (GPA) is a unified audio foundation model that integrates multiple core speech tasks within a single large language model (LLM) architecture.<n>GPA operates on a shared discrete audio token space and supports instruction-driven task induction, enabling a single autoregressive model to flexibly TTS, ASR, and VC.
• Score: 8.890811356340953
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional speech systems typically rely on separate, task-specific models for text-to-speech (TTS), automatic speech recognition (ASR), and voice conversion (VC), resulting in fragmented pipelines that limit scalability, efficiency, and cross-task generalization. In this paper, we present General-Purpose Audio (GPA), a unified audio foundation model that integrates multiple core speech tasks within a single large language model (LLM) architecture. GPA operates on a shared discrete audio token space and supports instruction-driven task induction, enabling a single autoregressive model to flexibly perform TTS, ASR, and VC without architectural modifications. This unified design combines a fully autoregressive formulation over discrete speech tokens, joint multi-task training across speech domains, and a scalable inference pipeline that achieves high concurrency and throughput. The resulting model family supports efficient multi-scale deployment, including a lightweight 0.3B-parameter variant optimized for edge and resource-constrained environments. Together, these design choices demonstrate that a unified autoregressive architecture can achieve competitive performance across diverse speech tasks while remaining viable for low-latency, practical deployment.

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