arXiv:2409.12319v1 Announce Type: cross
Abstract: Multimodal large language models (MLLMs) have recently become a focal point of research due to their formidable multimodal understanding capabilities. For example, in the audio and speech domains, an LLM can be equipped with (automatic) speech recognition (ASR) abilities by just concatenating the audio tokens, computed with an audio encoder, and the text tokens to achieve state-of-the-art results. On the contrary, tasks like visual and audio-visual speech recognition (VSR/AVSR), which also exploit noise-invariant lip movement information, have received little or no attention. To bridge this gap, we propose Llama-AVSR, a new MLLM with strong audio-visual speech recognition capabilities. It leverages pre-trained audio and video encoders to produce modality-specific tokens which, together with the text tokens, are processed by a pre-trained LLM (e.g., Llama3.1-8B) to yield the resulting response in an auto-regressive fashion. Llama-AVSR requires a small number of trainable parameters as only modality-specific projectors and LoRA modules are trained whereas the multi-modal encoders and LLM are kept frozen. We evaluate our proposed approach on LRS3, the largest public AVSR benchmark, and we achieve new state-of-the-art results for the tasks of ASR and AVSR with a WER of 0.81% and 0.77%, respectively. To bolster our results, we investigate the key factors that underpin the effectiveness of Llama-AVSR: the choice of the pre-trained encoders and LLM, the efficient integration of LoRA modules, and the optimal performance-efficiency trade-off obtained via modality-aware compression rates.
Analysis: Multimodal Large Language Models in Multimedia Information Systems
The concept of multimodal large language models (MLLMs) is a cutting-edge area of research that combines artificial intelligence, natural language processing, and computer vision to enhance the understanding of textual, audio, and visual information. MLLMs have gained significant attention due to their impressive capabilities in analyzing multimodal data and performing various tasks such as speech recognition, image captioning, and video understanding.
In this particular study, the focus is on the audio and visual domains, specifically audio-visual speech recognition (AVSR). While ASR (automatic speech recognition) has advanced significantly with the help of MLLMs, AVSR has received relatively little attention. AVSR is a challenging task as it requires understanding not only the audio but also the visual signals, particularly lip movement.
The proposed model, Llama-AVSR, aims to bridge this gap in AVSR research. It leverages pre-trained audio and video encoders to extract modality-specific tokens, which are then combined with text tokens and processed through a pre-trained LLM. By adopting an auto-regressive approach, Llama-AVSR generates highly accurate responses for both ASR and AVSR tasks.
One key aspect of Llama-AVSR is the use of frozen multi-modal encoders and LLM, which means that they are not fine-tuned during training. Instead, only modality-specific projectors and LoRA (Local Refinement Attention) modules are trained. This approach allows for efficient training and parameter optimization while still achieving state-of-the-art results.
The authors evaluate Llama-AVSR using the LRS3 benchmark, which is a widely-used dataset for AVSR. The results demonstrate the effectiveness of the proposed approach, achieving a Word Error Rate (WER) of 0.81% for ASR and 0.77% for AVSR, which are new state-of-the-art performances.
This study showcases the interdisciplinary nature of multimedia information systems, combining elements from audio processing, computer vision, and natural language understanding. By integrating audio and visual information, MLLMs like Llama-AVSR have the potential to revolutionize various applications such as speech recognition systems, virtual reality experiences, and interactive multimedia content.
Implications for Animations, Artificial Reality, Augmented Reality, and Virtual Realities
Animations, artificial reality, augmented reality, and virtual realities greatly benefit from the advancements in multimodal large language models such as Llama-AVSR. The ability to understand and process both audio and visual information opens up new possibilities for creating immersive and realistic experiences in these domains.
In the context of animations, MLLMs can enhance the creation and synchronization of animated characters’ lip movements with the corresponding dialogue or speech. By utilizing the lip movement information, as considered in AVSR, animations can be more accurate and lifelike. This can significantly improve the quality of animated movies, TV shows, and video games, bringing characters to life in a way that closely matches the intended audio.
For artificial reality, MLLMs can play a crucial role in bridging the gap between artificial intelligence and virtual environments. By understanding and responding to multimodal inputs from users, AI-powered virtual agents or characters can engage in more realistic and natural interactions. This can enhance the overall user experience, making artificial reality environments feel more immersive and interactive.
In augmented reality applications, MLLMs like Llama-AVSR can contribute to more accurate speech recognition and understanding. For example, in AR systems that involve voice commands or speech-based interactions, having a robust AVSR capability can improve the accuracy of speech recognition, enabling more intuitive and reliable interactions between users and augmented environments.
Virtual reality experiences can also benefit from the advancements in MLLMs. By analyzing both audio and visual cues, virtual reality systems can provide more realistic and context-aware simulations. For instance, within a virtual reality game, the recognition of audio-visual speech can be used to enhance the understanding of the player’s voice commands and facilitate more intelligent and immersive gameplay.
In conclusion, the development of multimodal large language models, exemplified by Llama-AVSR, has far-reaching implications for multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By combining expertise from multiple disciplines, these models open up exciting possibilities for advanced multimodal processing and more immersive and realistic experiences in various domains.