Speech-Omni-Lite: Portable Speech Interfaces for Vision-Language Models
Abstract
While large-scale omni-models have demonstrated impressive capabilities across various modalities, their strong performance heavily relies on massive multimodal data and incurs substantial computational costs. This work introduces Speech-Omni-Lite, a cost-efficient framework for extending pre-trained Visual-Language (VL) backbones with speech understanding and generation capabilities, while fully preserving the backbones' vision-language performance. Specifically, the VL backbone is equipped with two lightweight, trainable plug-and-play modules, a speech projector and a speech token generator, while keeping the VL backbone fully frozen. To mitigate the scarcity of spoken QA corpora, a low-cost data construction strategy is proposed to generate Question-Text Answer-Text-Speech (QTATS) data from existing ASR speech-text pairs, facilitating effective speech generation training. Experimental results show that, even with only thousands of hours of speech training data, Speech-Omni-Lite achieves excellent spoken QA performance, which is comparable to omni-models trained on millions of hours of speech data. Furthermore, the learned speech modules exhibit strong transferability across VL backbones.
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
This paper presents a cost-efficient framework for integrating speech capabilities into vision-language models, significantly advancing multimodal machine learning by reducing resource requirements and enhancing accessibility. The technical contributions, particularly in methodology and experimental validation, position this work as a meaningful advancement in the field.
Methodology
The methodology is innovative, introducing a cost-efficient framework that utilizes lightweight, trainable modules to extend pre-trained vision-language models with speech capabilities. The approach of keeping the backbone frozen while adding modular components is a significant advancement, allowing for efficient training and transferability across different backbones. The use of a novel data construction strategy (QTATS) to mitigate data scarcity is particularly noteworthy, as it leverages existing resources effectively.
Results & evaluation
The experimental results demonstrate strong performance in spoken QA tasks, achieving results comparable to larger models trained on extensive datasets. The evaluation metrics used, including WER and CER, provide a solid foundation for assessing the model's capabilities. However, the paper could benefit from more extensive ablation studies to further validate the contributions of individual components.
Reproducibility
The paper provides detailed descriptions of the model architecture and training procedures, which enhances reproducibility. However, the absence of a public code repository limits the ability for other researchers to replicate the results fully. Including a demo or project URL would have further supported reproducibility efforts.
Limitations
One limitation is the reliance on synthetic data for training the speech token generator, which may introduce biases or inconsistencies compared to real-world data. Additionally, the performance on ASR tasks is noted to be lower than that of larger models, indicating potential areas for improvement. The paper also does not address the computational requirements for the training of the lightweight modules, which could be a concern for smaller institutions.
Broader impact
The work has significant implications for democratizing access to advanced multimodal AI systems, as it reduces the computational burden and data requirements typically associated with training omni-modal models. This could lead to broader applications in accessibility technologies, particularly for individuals with disabilities who rely on speech interfaces. The framework also raises ethical considerations regarding the deployment of generative models, particularly in ensuring safety and alignment with societal values. This paper presents a cost-efficient framework for integrating speech capabilities into vision-language models, significantly advancing multimodal machine learning by reducing resource requirements and enhancing accessibility. The technical contributions, particularly in methodology and experimental validation, position this work as a meaningful advancement in the field.
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