Friday 28 February 2025
Recent advancements in large language models (LLMs) have led to significant progress in integrating visual and textual modalities. However, the role of speech in multimodal dialogue systems has received less attention. Researchers have proposed a carefully designed multi-stage training methodology that progressively trains LLMs to understand both visual and speech information, enabling fluent vision and speech interaction.
The approach preserves strong vision-language capacity while also allowing for efficient speech-to-speech dialogue capabilities without separate automatic speech recognition (ASR) and text-to-speech (TTS) modules. This accelerates multimodal end-to-end response speed. By comparing the method against state-of-the-art counterparts across benchmarks for image, video, and speech tasks, the model is shown to be equipped with both strong visual and speech capabilities, making near real-time vision and speech interaction possible.
The training and inference codes have been released, allowing researchers to build upon this work. This development has significant implications for human-computer interaction, enabling more natural and convenient interactions between humans and machines.
One of the key challenges in integrating visual and textual modalities is the fundamental difference in spatial information conveyed by images versus dynamic changes in time series data. These differences pose a challenge for simultaneous optimization of both modalities during training. The proposed methodology addresses this issue by incorporating speech data into the model, allowing it to learn from both visual and auditory cues.
The approach has been evaluated on various benchmarks, including image captioning, video question answering, and speech recognition tasks. Results show that the model achieves state-of-the-art performance in many of these tasks, demonstrating its ability to effectively integrate visual and speech information.
This research has significant implications for the development of multimodal dialogue systems, enabling more natural and efficient interactions between humans and machines. The release of the training and inference codes provides a foundation for further research in this area, allowing researchers to build upon and improve this work.
Cite this article: “Integrating Visual and Speech Modalities in Multimodal Dialogue Systems”, The Science Archive, 2025.
Large Language Models, Multimodal Dialogue Systems, Visual And Textual Modalities, Speech Recognition, Text-To-Speech, Automatic Speech Recognition, End-To-End Response Speed, Human-Computer Interaction, Image Captioning, Video Question Answering
