arXiv:2504.15066v1 Announce Type: new
Abstract: Incorporating visual modalities to assist Automatic Speech Recognition (ASR) tasks has led to significant improvements. However, existing Audio-Visual Speech Recognition (AVSR) datasets and methods typically rely solely on lip-reading information or speaking contextual video, neglecting the potential of combining these different valuable visual cues within the speaking context. In this paper, we release a multimodal Chinese AVSR dataset, Chinese-LiPS, comprising 100 hours of speech, video, and corresponding manual transcription, with the visual modality encompassing both lip-reading information and the presentation slides used by the speaker. Based on Chinese-LiPS, we develop a simple yet effective pipeline, LiPS-AVSR, which leverages both lip-reading and presentation slide information as visual modalities for AVSR tasks. Experiments show that lip-reading and presentation slide information improve ASR performance by approximately 8% and 25%, respectively, with a combined performance improvement of about 35%. The dataset is available at https://kiri0824.github.io/Chinese-LiPS/
Incorporating Multimodal Visual Cues for Audio-Visual Speech Recognition
Automatic Speech Recognition (ASR) tasks have greatly benefited from the inclusion of visual modalities. However, existing Audio-Visual Speech Recognition (AVSR) datasets and methods often focus solely on lip-reading or speaking contextual video, neglecting the potential of combining different valuable visual cues within the speaking context. In this paper, the authors introduce the Chinese-LiPS multimodal AVSR dataset and present the LiPS-AVSR pipeline, which leverages lip-reading and presentation slide information as visual cues for AVSR tasks.
The Chinese-LiPS dataset is a comprehensive collection comprising 100 hours of speech, video, and corresponding manual transcription. What sets this dataset apart is the inclusion of not only lip-reading information but also the presentation slides used by the speaker. This multi-disciplinary approach allows for a more holistic understanding of the audio-visual speech data, capturing the subtle nuances and context that improve ASR performance.
The LiPS-AVSR pipeline developed based on the Chinese-LiPS dataset demonstrates the effectiveness of leveraging multiple visual cues. The experiments conducted show that lip-reading information improves ASR performance by approximately 8%, while presentation slide information leads to a significant improvement of about 25%. When combined, the performance improvement reaches approximately 35%. This highlights the synergy of different visual cues and the potential for further enhancement in AVSR tasks.
This research embodies the multi-disciplinary nature of multimedia information systems, incorporating elements from speech recognition, computer vision, and human-computer interaction. By combining the analytical power of machine learning algorithms with visual and textual information, this work pushes the boundaries of AVSR systems and opens up new avenues for research.
Furthermore, the incorporation of visual cues extends beyond AVSR and has implications for other areas such as animations, artificial reality, augmented reality, and virtual realities. These technologies heavily rely on the integration of audio and visual information, and leveraging multimodal cues can greatly enhance the immersive experience and realism. The Chinese-LiPS dataset and the LiPS-AVSR pipeline serve as valuable resources for researchers and industry professionals working in these fields, providing a foundation for developing more advanced and accurate systems.
In conclusion, the release of the Chinese-LiPS multimodal AVSR dataset and the development of the LiPS-AVSR pipeline demonstrate the power of incorporating multiple visual cues for improved ASR performance. This work showcases the multi-disciplinary nature of multimedia information systems and has far-reaching implications for various domains. By combining lip-reading and presentation slide information, the LiPS-AVSR pipeline sets a new standard for AVSR systems and opens up exciting possibilities for further research and development.