HI-TransPA: Hearing Impairments Translation Personal Assistant
Abstract
To provide a unified and flexible solution for daily communication among hearing-impaired individuals, we introduce the Omni-Model paradigm into assistive technology and present HI-TransPA, an instruction-driven audio-visual personal assistant. The model fuses indistinct speech with high-frame-rate lip dynamics, enabling both translation and dialogue within a single multimodal framework. To tackle the challenges of noisy and heterogeneous raw data and the limited adaptability of existing Omni-Models to hearing-impaired speech, we construct a comprehensive preprocessing and curation pipeline that detects facial landmarks, isolates and stabilizes the lip region, and quantitatively assesses multimodal sample quality. These quality scores guide a curriculum learning strategy that first trains on clean, high-confidence samples and progressively incorporates harder cases to strengthen model robustness. We further adopt a SigLIP encoder combined with a Unified 3D-Resampler to efficiently encode high-frame-rate lip motion. Experiments on our purpose-built HI-Dialogue dataset show that HI-TransPA achieves state-of-the-art performance in both literal accuracy and semantic fidelity. This work establishes a foundation for applying Omni-Models to assistive communication technology, providing an end-to-end modeling framework and essential processing tools for future research.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
HI-TransPA introduces an innovative audio-visual personal assistant for hearing-impaired individuals, integrating translation and dialogue capabilities within a single framework. This comprehensive approach, leveraging advanced preprocessing and curriculum learning, addresses critical challenges in assistive technology and demonstrates significant improvements over existing models, marking a meaningful contribution to the field of machine learning and accessibility.
Methodology
The methodology presented in HI-TransPA is robust and innovative, integrating a preprocessing pipeline for lip dynamics and audio quality assessment, which is critical for handling the complexities of hearing-impaired speech. The use of a quality-guided curriculum learning strategy is particularly noteworthy, as it allows the model to adaptively learn from varying data quality, enhancing its robustness. The architecture leverages advanced components like the SigLIP encoder and Unified 3D-Resampler, which are well-suited for the task of multimodal integration. Overall, the approach is comprehensive and effectively addresses the challenges faced by existing assistive technologies.
Results & evaluation
The experimental evaluation is thorough, utilizing a purpose-built dataset (HI-Dialogue) that is well-curated for the task at hand. The results demonstrate clear advantages over existing models, with significant improvements in both character error rate (CER) and embedding similarity (EmbSim). The comparative analysis against various baselines, including ASR models and other Omni-Models, provides a solid foundation for the claims of state-of-the-art performance. However, the paper could benefit from more extensive ablation studies to further validate the contributions of each component.
Reproducibility
The paper lacks specific URLs for code or datasets, which hinders reproducibility. While the methodology is described in detail, the absence of a public repository or demo limits the ability of other researchers to replicate the results. Providing access to the HI-Dialogue dataset and the model code would significantly enhance reproducibility.
Limitations
One limitation is the reliance on a relatively small dataset, which may affect the generalizability of the model. Additionally, while the model shows improvements in accuracy, it does not address conversational fluency and empathy metrics, which are important for real-world applications. The focus on comprehension may overlook other aspects of dialogue generation that are crucial for user interaction.
Broader impact
The potential applications of HI-TransPA are significant, as it aims to enhance communication for individuals with hearing impairments, a large and often underserved population. By providing a unified solution for translation and dialogue, this work could lead to greater social inclusion and improved quality of life for users. The research also sets a precedent for future work in assistive technologies, encouraging further exploration of multimodal AI applications in accessibility. HI-TransPA introduces an innovative audio-visual personal assistant for hearing-impaired individuals, integrating translation and dialogue capabilities within a single framework. This comprehensive approach, leveraging advanced preprocessing and curriculum learning, addresses critical challenges in assistive technology and demonstrates significant improvements over existing models, marking a meaningful contribution to the field of machine learning and accessibility.
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