Cued-Agent: A Collaborative Multi-Agent System for Automatic Cued Speech Recognition

• URL: http://arxiv.org/abs/2508.00391v1
• Date: Fri, 01 Aug 2025 07:40:39 GMT
• Title: Cued-Agent: A Collaborative Multi-Agent System for Automatic Cued Speech Recognition
• Abstract summary: Cued Speech (CS) is a visual communication system that combines lip-reading with hand coding to facilitate communication for individuals with hearing impairments.<n> Automatic CS Recognition (ACSR) aims to convert CS hand gestures and lip movements into text via AI-driven methods.<n>We propose the first collaborative multi-agent system for ACSR, named Cued-Agent.
• Score: 17.451829471077858
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Cued Speech (CS) is a visual communication system that combines lip-reading with hand coding to facilitate communication for individuals with hearing impairments. Automatic CS Recognition (ACSR) aims to convert CS hand gestures and lip movements into text via AI-driven methods. Traditionally, the temporal asynchrony between hand and lip movements requires the design of complex modules to facilitate effective multimodal fusion. However, constrained by limited data availability, current methods demonstrate insufficient capacity for adequately training these fusion mechanisms, resulting in suboptimal performance. Recently, multi-agent systems have shown promising capabilities in handling complex tasks with limited data availability. To this end, we propose the first collaborative multi-agent system for ACSR, named Cued-Agent. It integrates four specialized sub-agents: a Multimodal Large Language Model-based Hand Recognition agent that employs keyframe screening and CS expert prompt strategies to decode hand movements, a pretrained Transformer-based Lip Recognition agent that extracts lip features from the input video, a Hand Prompt Decoding agent that dynamically integrates hand prompts with lip features during inference in a training-free manner, and a Self-Correction Phoneme-to-Word agent that enables post-process and end-to-end conversion from phoneme sequences to natural language sentences for the first time through semantic refinement. To support this study, we expand the existing Mandarin CS dataset by collecting data from eight hearing-impaired cuers, establishing a mixed dataset of fourteen subjects. Extensive experiments demonstrate that our Cued-Agent performs superbly in both normal and hearing-impaired scenarios compared with state-of-the-art methods. The implementation is available at https://github.com/DennisHgj/Cued-Agent.

Related papers

• HyLaT: Efficient Multi-Agent Communication via Hybrid Latent-Text Protocol [54.411053382333996]
  HyLaT is a hybrid latent-text communication protocol that transmits elaborate cognitive signals through a latent channel for efficiency.<n>We introduce a two-stage training framework combining single-agent hybrid generation learning and multi-agent interactive co-training, enabling agents to generate and interpret hybrid messages.<n>Experiments demonstrate that HyLaT reduces communication overhead significantly while maintaining competitive task performance, with strong generalization and robustness across diverse settings.
  arXiv  Detail & Related papers  (2026-05-25T04:50:15Z)
• Learning to Communicate: Toward End-to-End Optimization of Multi-Agent Language Systems [30.327850022896286]
  We propose DiffMAS, a training framework that treats latent communication as a learnable component of multi-agent systems.<n>Experiments show that DiffMAS consistently improves reasoning accuracy and decoding stability over single-agent inference, text-based multi-agent systems, and prior latent communication methods.
  arXiv  Detail & Related papers  (2026-04-23T15:53:25Z)
• When Only the Final Text Survives: Implicit Execution Tracing for Multi-Agent Attribution [10.973058523304042]
  We introduce IET (Implicit Execution Tracing), a metadata-independent framework that enables token-level attribution directly from generated text.<n>During generation, agent-specific keyed signals are embedded into the token distribution, transforming the text into a self-describing execution trace detectable only with a secret key.<n>At detection time, a transition-aware scoring method identifies agent handover points and reconstructs the interaction graph.
  arXiv  Detail & Related papers  (2026-03-18T07:34:51Z)
• AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction [70.60422261117816]
  We propose a new framework that rethinks multi-agent coordination through a sequential structure rather than a graph structure.<n>Our method focuses on two key directions: (1) Next-Agent Prediction, which selects the most suitable agent role at each step, and (2) Next-Context Selection, which enables each agent to selectively access relevant information from any previous step.
  arXiv  Detail & Related papers  (2025-06-21T18:34:43Z)
• Chain-of-Thought Training for Open E2E Spoken Dialogue Systems [57.77235760292348]
  End-to-end (E2E) spoken dialogue systems preserve full differentiability and capture non-phonemic information.<n>We propose a chain-of-thought (CoT) formulation to ensure that training on conversational data remains closely aligned with the multimodal language model.<n>Our method achieves over 1.5 ROUGE-1 improvement over the baseline, successfully training spoken dialogue systems on publicly available human-human conversation datasets.
  arXiv  Detail & Related papers  (2025-05-31T21:43:37Z)
• VQ-CTAP: Cross-Modal Fine-Grained Sequence Representation Learning for Speech Processing [81.32613443072441]
  For tasks such as text-to-speech (TTS), voice conversion (VC), and automatic speech recognition (ASR), a cross-modal fine-grained (frame-level) sequence representation is desired.<n>We propose a method called Quantized Contrastive Token-Acoustic Pre-training (VQ-CTAP), which uses the cross-modal sequence transcoder to bring text and speech into a joint space.
  arXiv  Detail & Related papers  (2024-08-11T12:24:23Z)
• Computation and Parameter Efficient Multi-Modal Fusion Transformer for Cued Speech Recognition [48.84506301960988]
  Cued Speech (CS) is a pure visual coding method used by hearing-impaired people. automatic CS recognition (ACSR) seeks to transcribe visual cues of speech into text.
  arXiv  Detail & Related papers  (2024-01-31T05:20:29Z)
• SpeechAgents: Human-Communication Simulation with Multi-Modal Multi-Agent Systems [53.94772445896213]
  Large Language Model (LLM)-based multi-agent systems have demonstrated promising performance in simulating human society. We propose SpeechAgents, a multi-modal LLM based multi-agent system designed for simulating human communication.
  arXiv  Detail & Related papers  (2024-01-08T15:01:08Z)
• TESS: A Multi-intent Parser for Conversational Multi-Agent Systems with Decentralized Natural Language Understanding Models [6.470108226184637]
  Multi-agent systems complicate the natural language understanding of user intents. We propose an efficient parsing and orchestration pipeline algorithm to service multi-intent utterances from the user.
  arXiv  Detail & Related papers  (2023-12-19T03:39:23Z)
• Cross-Modal Mutual Learning for Cued Speech Recognition [10.225972737967249]
  We propose a transformer based cross-modal mutual learning framework to prompt multi-modal interaction. Our model forces modality-specific information of different modalities to pass through a modality-invariant codebook. We establish a novel large-scale multi-speaker CS dataset for Mandarin Chinese.
  arXiv  Detail & Related papers  (2022-12-02T10:45:33Z)
• Any-to-Many Voice Conversion with Location-Relative Sequence-to-Sequence Modeling [61.351967629600594]
  This paper proposes an any-to-many location-relative, sequence-to-sequence (seq2seq), non-parallel voice conversion approach. In this approach, we combine a bottle-neck feature extractor (BNE) with a seq2seq synthesis module. Objective and subjective evaluations show that the proposed any-to-many approach has superior voice conversion performance in terms of both naturalness and speaker similarity.
  arXiv  Detail & Related papers  (2020-09-06T13:01:06Z)

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.