Modeling of Multimodal Speech Recognition and Language Processing

Dear Colleagues,

This Special Issue, ‘Modeling of Multimodal Speech Recognition and Language Processing,’ aims to delve into the rapidly evolving landscape of automatic speech recognition (ASR) and language processing. It seeks to collate papers exploring innovative approaches that bridge the gap between human speech comprehension and computational interpretation, as well as that emphasize the development of novel techniques to enhance ASR and language modeling, particularly in challenging environments such as diverse noisy settings, multimodal contexts, and multi-lingual speech recognition.

By concentrating on challenging real-world scenarios, we encourage researchers to push the boundaries of existing knowledge and contribute ground-breaking solutions to the field. Furthermore, this Special Issue is designed to provide a comprehensive resource for researchers, both newcomers and experts, by presenting cutting-edge research, methodologies, and insights that are directly applicable to real-world ASR and language processing challenges.

In relation to the existing approaches, this Special Issue seeks to build upon the foundation laid by prior research in ASR and language processing, as well as extend and enhance the existing literature by focusing on emerging challenges, such as multimodal recognition and security concerns, that have gained prominence in recent years. By addressing these gaps in the literature, we aim to offer a forward-looking perspective on ASR and language processing, showcasing practical solutions and insights that align with contemporary demands. Researchers can expect to find valuable references and inspiration to address the most pressing issues in the field, making this Special Issue a pivotal addition to the existing body of work.

Topics of interests include, but are not limited to, the following:

• Robust speech recognition;
• Language modeling;
• Multi-lingual speech recognition;
• Audio-visual speech recognition;
• Fast decoding techniques;
• Representation learning for audio, text, or/and vision;
• Speaker recognition for speech recognition;
• Audio security and adversarial attacks on speech recognition models;
• Large speech models;
• Large language models for speech recognition.

Dr. Xiaoxue Gao
Dr. Xinyuan Qian
Dr. Ruijie Tao
Dr. Malu Zhang
Dr. Zhaojie Luo
Guest Editors

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