arXiv:2408.16564v1 Announce Type: new
Abstract: Humans naturally perform audiovisual speech recognition (AVSR), enhancing the accuracy and robustness by integrating auditory and visual information. Spiking neural networks (SNNs), which mimic the brain’s information-processing mechanisms, are well-suited for emulating the human capability of AVSR. Despite their potential, research on SNNs for AVSR is scarce, with most existing audio-visual multimodal methods focused on object or digit recognition. These models simply integrate features from both modalities, neglecting their unique characteristics and interactions. Additionally, they often rely on future information for current processing, which increases recognition latency and limits real-time applicability. Inspired by human speech perception, this paper proposes a novel human-inspired SNN named HI-AVSNN for AVSR, incorporating three key characteristics: cueing interaction, causal processing and spike activity. For cueing interaction, we propose a visual-cued auditory attention module (VCA2M) that leverages visual cues to guide attention to auditory features. We achieve causal processing by aligning the SNN’s temporal dimension with that of visual and auditory features and applying temporal masking to utilize only past and current information. To implement spike activity, in addition to using SNNs, we leverage the event camera to capture lip movement as spikes, mimicking the human retina and providing efficient visual data. We evaluate HI-AVSNN on an audiovisual speech recognition dataset combining the DVS-Lip dataset with its corresponding audio samples. Experimental results demonstrate the superiority of our proposed fusion method, outperforming existing audio-visual SNN fusion methods and achieving a 2.27% improvement in accuracy over the only existing SNN-based AVSR method.
Expert Commentary: The Potential of Spiking Neural Networks for Audiovisual Speech Recognition
Audiovisual speech recognition (AVSR) is a fascinating area of research that aims to integrate auditory and visual information to enhance the accuracy and robustness of speech recognition systems. In this paper, the researchers focus on the potential of spiking neural networks (SNNs) as an effective model for AVSR. As a commentator with expertise in the field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, I find this study highly relevant and interesting.
One of the key contributions of this paper is the development of a human-inspired SNN called HI-AVSNN. By mimicking the brain’s information-processing mechanisms, SNNs have the advantage of capturing the temporal dynamics of audiovisual speech signals. This is crucial for accurate AVSR, as speech communication involves complex interactions between auditory and visual modalities.
The authors propose three key characteristics for their HI-AVSNN model: cueing interaction, causal processing, and spike activity. Cueing interaction refers to the use of visual cues to guide attention to auditory features. This is inspired by how humans naturally focus their attention on relevant visual information during speech perception. By incorporating cueing interaction into their model, the researchers aim to improve the fusion of auditory and visual information.
Causal processing is another important characteristic of the HI-AVSNN model. By aligning the temporal dimension of the SNN with that of visual and auditory features, and utilizing only past and current information through temporal masking, the model can operate in a causal manner. This is essential for real-time applicability, as relying on future information would increase recognition latency.
The third characteristic, spike activity, is implemented by leveraging the event camera to capture lip movement as spikes. This approach mimics the human retina, which is highly efficient in processing visual data. By incorporating the event camera and SNNs, the model can effectively process visual cues and achieve efficient AVSR.
From a multi-disciplinary perspective, this study combines concepts from neuroscience, computer vision, and artificial intelligence. The integration of auditory and visual modalities requires a deep understanding of human perception, the analysis of audiovisual signals, and the development of advanced machine learning models. The authors successfully bridge these disciplines to propose an innovative approach for AVSR.
In the wider field of multimedia information systems, including animations, artificial reality, augmented reality, and virtual realities, AVSR plays a crucial role. Accurate recognition of audiovisual speech is essential for applications such as automatic speech recognition, video conferencing, virtual reality communication, and human-computer interaction. The development of a robust and efficient AVSR system based on SNNs could greatly enhance these applications and provide a more immersive and natural user experience.
In conclusion, the paper presents a compelling case for the potential of spiking neural networks in audiovisual speech recognition. The HI-AVSNN model incorporates important characteristics inspired by human speech perception and outperforms existing methods in terms of accuracy. As further research and development in this area continue, we can expect to see advancements in multimedia information systems and the integration of audiovisual modalities in various applications.