# FireRedChat: A Pluggable, Full-Duplex Voice Interaction System with Cascaded and Semi-Cascaded Implementations

arXiv: https://arxiv.org/abs/2509.06502 · HTML: https://mlsanity.com/paper/2509.06502/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2025.

## Built on by (most cited first, 18 of 18)

- [Can Speech LLMs Think while Listening?](https://mlsanity.com/paper/2510.07497/) (2025)
- [Chronological Thinking in Full-Duplex Spoken Dialogue Language Models](https://mlsanity.com/paper/2510.05150/) (2025)
- [Toward Native Multimodal Modeling: A Roadmap](https://mlsanity.com/paper/2605.25343/) (2026)
- [Hierarchical Acoustic-Semantic Modeling: Modality Separation and Semantic Coherence for Full-Duplex SLMs](https://mlsanity.com/paper/2607.06540/) (2026)
- [F-Actor: Controllable Conversational Behaviour in Full-Duplex Models](https://mlsanity.com/paper/2601.11329/) (2026)
- [Discourse-Aware Dual-Track Streaming Response for Low-Latency Spoken Dialogue Systems](https://mlsanity.com/paper/2602.23266/) (2026)
- [Vox-Infinity: Benchmarking the Limits of Long-Context Spoken Language Models](https://mlsanity.com/paper/2609.22452/) (2026)
- [Unit-Based Agent for Semi-Cascaded Full-Duplex Dialogue Systems](https://mlsanity.com/paper/2601.20230/) (2026)
- [RelayS2S: A Dual-Path Speculative Generation for Real-Time Dialogue](https://mlsanity.com/paper/2603.23346/) (2026)
- [PolySpeech-100: A Large-Scale Benchmark for Speech Understanding Across 100+ Languages and Dialects](https://mlsanity.com/paper/2606.01016/) (2026)
- [RetroThinker: Enabling Retrospective Thinking in Speech LLMs](https://mlsanity.com/paper/2609.11864/) (2026)
- [Enabling Streaming User Transcription in Full-Duplex Speech-to-Speech Models](https://mlsanity.com/paper/2609.15759/) (2026)
- [Character Beyond Speech: Leveraging Role-Playing Evaluation in Audio Large Language Models via Reinforcement Learning](https://mlsanity.com/paper/2604.13804/) (2026)
- [UAF: A Unified Audio Front-end LLM for Full-Duplex Speech Interaction](https://mlsanity.com/paper/2604.19221/) (2026)
- [IRAF: Interference-Resilient Adaptive Fusion for Noise-Robust End-to-End Full-Duplex Spoken Dialogue Systems](https://mlsanity.com/paper/2606.06559/) (2026)
- [Enabling Proactive Spoken Turns via a Generalized Style-Aware Full-Duplex Framework](https://mlsanity.com/paper/2608.28630/) (2026)
- [XTurnix: Large-Scale Self-Supervised Turn Control through Two-State Binary Decisions](https://mlsanity.com/paper/2610.04400/) (2026)
- [DuplexAgent-RSI: Recursive Harness Improvement for Full-Duplex Voice Agent Collaboration](https://mlsanity.com/paper/2610.11299/) (2026)

## Abstract (arXiv)

Full-duplex voice interaction allows users and agents to speak simultaneously with controllable barge-in, enabling lifelike assistants and customer service. Existing solutions are either end-to-end, difficult to design and hard to control, or modular pipelines governed by turn-taking controllers that ease upgrades and per-module optimization; however, prior modular frameworks depend on non-open components and external providers, limiting holistic optimization. In this work, we present a complete, practical full-duplex voice interaction system comprising a turn-taking controller, an interaction module, and a dialogue manager. The controller integrates streaming personalized VAD (pVAD) to suppress false barge-ins from noise and non-primary speakers, precisely timestamp primary-speaker segments, and explicitly enable primary-speaker barge-ins; a semantic end-of-turn detector improves stop decisions. It upgrades heterogeneous half-duplex pipelines, cascaded, semi-cascaded, and speech-to-speech, to full duplex. Using internal models, we implement cascaded and semi-cascaded variants; the semi-cascaded one captures emotional and paralinguistic cues, yields more coherent responses, lowers latency and error propagation, and improves robustness. A dialogue manager extends capabilities via tool invocation and context management. We also propose three system-level metrics, barge-in, end-of-turn detection accuracy, and end-to-end latency, to assess naturalness, control accuracy, and efficiency. Experiments show fewer false interruptions, more accurate semantic ends, and lower latency approaching industrial systems, enabling robust, natural, real-time full-duplex interaction. Demos: https://fireredteam.github.io/demos/firered_chat.