arXiv:2409.02266v1 Announce Type: cross
Abstract: In this paper, we propose long short term memory speech enhancement network (LSTMSE-Net), an audio-visual speech enhancement (AVSE) method. This innovative method leverages the complementary nature of visual and audio information to boost the quality of speech signals. Visual features are extracted with VisualFeatNet (VFN), and audio features are processed through an encoder and decoder. The system scales and concatenates visual and audio features, then processes them through a separator network for optimized speech enhancement. The architecture highlights advancements in leveraging multi-modal data and interpolation techniques for robust AVSE challenge systems. The performance of LSTMSE-Net surpasses that of the baseline model from the COG-MHEAR AVSE Challenge 2024 by a margin of 0.06 in scale-invariant signal-to-distortion ratio (SISDR), $0.03$ in short-time objective intelligibility (STOI), and $1.32$ in perceptual evaluation of speech quality (PESQ). The source code of the proposed LSTMSE-Net is available at url{https://github.com/mtanveer1/AVSEC-3-Challenge}.
Expert Commentary: Enhancing Speech Signals using LSTMSE-Net: A Multimodal Approach
In this groundbreaking research paper, the authors propose a novel audio-visual speech enhancement method called LSTMSE-Net. The primary objective of this method is to leverage the complementary nature of visual and audio information to enhance the quality of speech signals. By combining visual and audio features, the system achieves remarkable performance improvements compared to the baseline model in various evaluation metrics such as scale-invariant signal-to-distortion ratio (SISDR), short-time objective intelligibility (STOI), and perceptual evaluation of speech quality (PESQ).
The key innovation of LSTMSE-Net lies in its ability to effectively extract visual features using VisualFeatNet (VFN) and audio features using an encoder-decoder model. The system then concatenates and processes these features through a separator network, which results in optimized speech enhancement. The utilization of multimodal data and interpolation techniques demonstrates the multi-disciplinary nature of this research, combining concepts from multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
With the increasing availability of audio-visual data, the field of AVSE has gained significant attention in recent years. By exploiting the complementary information present in audio and visual signals, researchers aim to improve speech signal quality in various applications, such as speech recognition systems, hearing aids, and teleconferencing. LSTMSE-Net represents a notable contribution to this field by providing an advanced and efficient solution for speech enhancement.
The performance evaluation of LSTMSE-Net against the baseline model in the COG-MHEAR AVSE Challenge 2024 showcases its superiority. The margin of improvement in various metrics highlights the effectiveness of the proposed method. The scale-invariant signal-to-distortion ratio (SISDR) improvement of 0.06, short-time objective intelligibility (STOI) improvement of 0.03, and perceptual evaluation of speech quality (PESQ) improvement of 1.32 demonstrate its significant impact.
Furthermore, the availability of the source code for LSTMSE-Net on GitHub encourages collaboration and further research in the field. This open-source approach fosters progress and innovation by enabling researchers to build upon the proposed method and explore new ideas and improvements.
In conclusion, LSTMSE-Net presents a sophisticated audio-visual speech enhancement method that leverages multimodal data and interpolation techniques. The performance improvements demonstrated in comparison to the baseline model signal its potential in advancing the fields of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. This research lays the foundation for future advancements in AVSE and continues to push the boundaries of speech enhancement technologies.