Research on Multilingual Speech Recognition Based on Streaming End-to-End Speech-Language Integrated Modeling Algorithm

End-to-end Automatic Speech Recognition (ASR) is a technology that directly converts speech into text and has received extensive attention and research in recent years. From an industrial perspective, this paper provides an overview of recent advances in end-to-end models in the current ASR space and techniques to address challenges in real-world applications. First, three common end-to-end models, CTC, AED, and RNN-T are presented, then their strengths, weaknesses, and applicable scenarios are analyzed. Two learning strategies are then presented: multitask learning and self-supervised learning, which shows how the performance and generalization of the end-to-end model improve. Then, cross-language modeling and adaptive technology are introduced, and how they solve the problem of multilingual speech and personalized recognition is explained. Finally, some new models such as Transformer, Conformer, and UniSpeech are introduced. It shows how they use attention mechanisms and self-supervised learning to improve recognition accuracy and efficiency. This paper is intended to provide a comprehensive and useful reference for ASR researchers and engineers.