# All-in-One ASR: Unifying Encoder-Decoder Models of CTC, Attention, and Transducer in Dual-Mode ASR

arXiv: https://arxiv.org/abs/2512.11543 · HTML: https://mlsanity.com/paper/2512.11543/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2025.

## Built on by (most cited first, 2 of 2)

- [Reducing the Offline-Streaming Gap for Unified ASR Transducer with Consistency Regularization](https://mlsanity.com/paper/2604.19079/) (2026)
- [TRADE: Transducer-Augmented Decoder for Speech LLM](https://mlsanity.com/paper/2606.08486/) (2026)

## Abstract (arXiv)

This paper proposes a unified framework, All-in-One ASR, that allows a single model to support multiple automatic speech recognition (ASR) paradigms, including connectionist temporal classification (CTC), attention-based encoder-decoder (AED), and Transducer, in both offline and streaming modes. While each ASR architecture offers distinct advantages and trade-offs depending on the application, maintaining separate models for each scenario incurs substantial development and deployment costs. To address this issue, we introduce a multi-mode joiner that enables seamless integration of various ASR modes within a single unified model. Experiments show that All-in-One ASR significantly reduces the total model footprint while matching or even surpassing the recognition performance of individually optimized ASR models. Furthermore, joint decoding leverages the complementary strengths of different ASR modes, yielding additional improvements in recognition accuracy.