Multimodal Streaming Speech Synthesis and Zero-Sample Clone Framework for Smart Education
DOI:
https://doi.org/10.54097/0wxf3x04Keywords:
Multimodal fusion; streaming speech synthesis; zero-sample speech cloning; large language model; smart education.Abstract
With the deepening of the digital transformation of education, smart education has put forward higher requirements for natural and expressive real-time voice interaction technologies. However, traditional speech synthesis (TTS) systems face core challenges such as insufficient understanding of professional domain terms, monotonous emotional expression, and the lack of cross-modal collaboration in educational scenarios, making it difficult to meet the needs of immersive and interactive teaching. To overcome these limitations, this paper proposes a multimodal end-to-end streaming speech synthesis and intelligent processing framework for educational scenarios. Firstly, this framework builds a multimodal fusion network based on cross-modal attention mechanisms, which dynamically aligns text semantics, acoustic features, and speaker identity at multiple scales of phonemes, syllables, and sentences, significantly improving the naturalness and semantic consistency of the synthesized speech. Secondly, in terms of personalized speech modeling, the system introduces zero-sample speech cloning technology that integrates semantic understanding of large language models (LLMs) and progressive fine-tuning strategies, enabling high-fidelity replication of teacher-specific voice and cross-language synthesis with only a few seconds of audio samples. To meet the low latency requirements of real-time classroom interaction, the architecture integrates a streaming generation engine based on Chunk-Aware Causal Flow Matching, effectively supporting generation and transmission simultaneously, strictly controlling the system's end-to-end latency within 150 milliseconds. Experimental verification and system analysis show that this multi-task joint optimization framework can precisely handle speechization of complex subject content, adaptively adjust teaching emotional expression, and provide a solid multimodal speech technology foundation for building a highly inclusive and personalized intelligent education ecosystem.
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