X-Talk: On the Underestimated Potential of Modular Speech-to-Speech Dialogue System

2025-12-21

AI Summary

Current end-to-end speech dialogue systems struggle to jointly optimize multiple speech-related objectives within a single model, while the modular speech-to-speech (S2S) paradigm remains underexploited. This paper introduces X-Talk—the first open-source, low-latency modular S2S dialogue system—featuring a cascaded architecture that integrates voice activity detection, speech enhancement, automatic speech recognition (ASR), multimodal understanding, emotion and environmental sound analysis, retrieval-augmented generation (RAG), and tool-augmented large language models. Through systematic optimization, we achieve sub-second end-to-end latency (<1 s) while preserving full modularity—a first in the literature. X-Talk maintains competitive performance alongside enhanced interpretability, task adaptability, and extensibility. This work challenges the dominant “monolithic end-to-end” paradigm, offering a novel pathway toward robust, controllable, and evolvable speech dialogue systems.

Technology Category

• Natural Language Processing: Conversational AI/Dialog Systems
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

We present X-Talk, an open-source framework that champions a decoupled, modular design for LLM-driven speech-to-speech (S2S) systems. While the dominant trend favors end-to-end (E2E) modeling to optimize information flow, these "omni-models" often struggle to balance the competing objectives of complex speech tasks within a single network. X-Talk challenges this paradigm by demonstrating that a systematically optimized cascaded pipeline can achieve sub-second latency without sacrificing modular flexibility. Our framework seamlessly integrates specialized front-end components (e.g., VAD, speech enhancement) and diverse understanding models (e.g., ASR, emotion, and environmental sound analysis) with LLM capabilities like retrieval-augmented generation (RAG) and tool use. By revitalizing the cascaded approach, X-Talk highlights the underestimated potential of modular S2S systems and provides a robust foundation for future research and applications.

Problem

• Addresses limitations of end-to-end speech-to-speech systems
• Proposes a modular, cascaded framework for flexible speech processing
• Integrates specialized components with LLM capabilities for enhanced performance

Innovation

• Modular design replaces end-to-end speech systems
• Cascaded pipeline achieves sub-second latency with flexibility
• Integrates specialized components with LLM capabilities