Enabling Streaming User Transcription in Full-Duplex Speech-to-Speech Models

• URL: http://arxiv.org/abs/2609.15759v1
• Date: Mon, 14 Sep 2026 15:44:18 GMT
• Title: Enabling Streaming User Transcription in Full-Duplex Speech-to-Speech Models
• Abstract summary: We propose a method to add streaming capabilities to an existing S2S model.<n>Our approach requires minimal additional parameters and no significant changes to the base S2S model.
• Score: 54.83270723528963
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Full-duplex speech-to-speech (S2S) models enable natural conversational AI by allowing simultaneous listening and speaking. However, these models typically lack inherent user speech transcription, which is essential for applications such as conversation logging, accessibility features, and quality monitoring. In this work, we propose an efficient method to add streaming ASR capabilities to an existing duplex S2S model by introducing a lightweight ASR head in parallel to the agent text head. Our approach requires minimal additional parameters and no significant architectural changes to the base S2S model, enabling real-time user transcription while preserving full-duplex conversational capabilities including turn-taking and barge-in handling. Experimental results demonstrate that our method achieves streaming average WER of 10.21% on the HuggingFace Open ASR Leaderboard within the duplex S2S framework. Additionally, we show that the same architecture trained as a standalone streaming ASR model achieves competitive results (7.73% WER) compared to current SOTA models. We will open-source our training and inference code to facilitate further research in joint streaming ASR and S2S modeling.

Related papers

• Covo-Audio Technical Report [61.09708870154148]
  Covo-Audio, a 7B-end LALM, directly processes continuous audio inputs and generates audio outputs within a single unified architecture.<n>Covo-Audio-Chat, a dialogue-oriented variant, demonstrates semantic strong spoken conversational abilities.
  arXiv  Detail & Related papers  (2026-02-10T14:31:11Z)
• SALM-Duplex: Efficient and Direct Duplex Modeling for Speech-to-Speech Language Model [28.42203609938444]
  We propose a novel duplex speech to speech (S2S) architecture featuring continuous user inputs and agent outputs with channel fusion.<n>Using a pretrained streaming for user input enables the first duplex S2S model without requiring speech pretrain.<n> Experimental results show that the proposed model outperforms previous duplex models in reasoning, turn-taking, and barge-in abilities.
  arXiv  Detail & Related papers  (2025-05-21T15:48:30Z)
• CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models [74.80386066714229]
  We present an improved streaming speech synthesis model, CosyVoice 2.<n>Specifically, we introduce finite-scalar quantization to improve codebook utilization of speech tokens.<n>We develop a chunk-aware causal flow matching model to support various synthesis scenarios.
  arXiv  Detail & Related papers  (2024-12-13T12:59:39Z)
• Joint Optimization of Streaming and Non-Streaming Automatic Speech Recognition with Multi-Decoder and Knowledge Distillation [44.94458898538114]
  We present joint optimization of streaming and non-streaming ASR based on multi-decoder and knowledge distillation. Evaluation results show 2.6%-5.3% relative character error rate reductions (CERR) on CSJ for streaming ASR, and 8.3%-9.7% relative CERRs for non-streaming ASR within a single model.
  arXiv  Detail & Related papers  (2024-05-22T10:17:30Z)
• Streaming Speech-to-Confusion Network Speech Recognition [19.720334657478475]
  We present a novel streaming ASR architecture that outputs a confusion network while maintaining limited latency. We show that 1-best results of our model are on par with a comparable RNN-T system. We also show that our model outperforms a strong RNN-T baseline on a far-field voice assistant task.
  arXiv  Detail & Related papers  (2023-06-02T20:28:14Z)
• Deliberation Model for On-Device Spoken Language Understanding [69.5587671262691]
  We propose a novel deliberation-based approach to end-to-end (E2E) spoken language understanding (SLU) We show that our approach can significantly reduce the degradation when moving from natural speech to synthetic speech training.
  arXiv  Detail & Related papers  (2022-04-04T23:48:01Z)
• Streaming Speaker-Attributed ASR with Token-Level Speaker Embeddings [53.11450530896623]
  This paper presents a streaming speaker-attributed automatic speech recognition (SA-ASR) model that can recognize "who spoke what" Our model is based on token-level serialized output training (t-SOT) which was recently proposed to transcribe multi-talker speech in a streaming fashion. The proposed model achieves substantially better accuracy than a prior streaming model and shows comparable or sometimes even superior results to the state-of-the-art offline SA-ASR model.
  arXiv  Detail & Related papers  (2022-03-30T21:42:00Z)
• Dual-mode ASR: Unify and Improve Streaming ASR with Full-context Modeling [76.43479696760996]
  We propose a unified framework, Dual-mode ASR, to train a single end-to-end ASR model with shared weights for both streaming and full-context speech recognition. We show that the latency and accuracy of streaming ASR significantly benefit from weight sharing and joint training of full-context ASR.
  arXiv  Detail & Related papers  (2020-10-12T21:12:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.