aiqbench analysis
DoubleHelix introduces a structured, iterative framework for audio-visual speech recognition that significantly improves robustness in noisy environments through adaptive feature enhancement and learned alignment constraints. By treating cross-modal interaction as a multi-turn process rather than a single-step operation, the model achieves superior word error rates on clean audio while maintaining performance under babble-noise conditions. This work advances multimodal machine learning architectures by integrating degradation-aware gating mechanisms directly into the fusion pipeline.
DoubleHelix: Structured Cross-Modal Fusion for Audio-Visual Speech Recognition with LLMs
arXiv:2607.29112v1 Announce Type: cross Abstract: Audio-visual speech recognition (AVSR) relies on effective fusion of audio and visual modalities, yet existing approaches treat cross-modal interaction as a single-step operation without structured iterative refinement. We present DoubleHelix, a multimodal fusion framework that reformulates fusion as an iterative cross-modal interaction process with adaptive degradation-aware enhancement. The framework comprises three components including ReverseParallelHelix for multi-turn structured interaction with learned alignment constraints, QualitySensor for learning degradation-aware gating signals, and HelixReplication for consistency-guided conditional feature enhancement. Experiments on LRS3 demonstrate that DoubleHelix achieves 0.68% WER on clean audio, outperforming previous best results by 5.6% relative improvement under matched backbone settings. Comprehensive ablation studies validate each component contribution, including targeted analysis of design choices such as asymmetric pathway weighting. The framework shows improved robustness under evaluated babble-noise conditions, achieving 11.6% WER at SNR -5dB.
Abstract:Audio-visual speech recognition (AVSR) relies on effective fusion of audio and visual modalities, yet existing approaches treat cross-modal interaction as a single-step operation without structured iterative refinement. We present DoubleHelix, a multimodal fusion framework that reformulates fusion as an iterative cross-modal interaction process with adaptive degradation-aware enhancement. The framework comprises three components including ReverseParallelHelix for multi-turn structured interaction with learned alignment constraints, QualitySensor for learning degradation-aware gating signals, and HelixReplication for consistency-guided conditional feature enhancement. Experiments on LRS3 demonstrate that DoubleHelix achieves 0.68% WER on clean audio, outperforming previous best results by 5.6% relative improvement under matched backbone settings. Comprehensive ablation studies validate each component contribution, including targeted analysis of design choices such as asymmetric pathway weighting. The framework shows improved robustness under evaluated babble-noise conditions, achieving 11.6% WER at SNR -5dB.
Sound (cs.SD); Artificial Intelligence (cs.AI)
arXiv:2607.29112 [cs.SD]
(or arXiv:2607.29112v1 [cs.SD] for this version)
https://doi.org/10.48550/arXiv.2607.29112 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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