Semantically Aligned Soundscape from Text: A Keyword-Guided Lightweight Generation Framework and Applicability in Reading Scenarios

Text-to-audio (TTA) generation often suffers from inconsistencies between audio output and textual input due to insufficient representation of textual details. In this paper, we present a lightweight keyword-guided generation framework for soundscape synthesis that explicitly maps spatiotemporal cues in text to the corresponding sound effects. Based on this framework, we developed two generative systems and validated their flexibility and effectiveness. System I leverages the large language model (LLM) to enhance semantic representation by introducing a structured mapping mechanism. We constructed a small-scale dataset based on this system and fine-tuned a state-of-the-art TTA model. System II employs traditional semantic analysis and generates soundscape using a predefined base dataset and an effect-mapping set. A subjective evaluation involving 24 participants demonstrated that the soundscape generated using the proposed approach yielded higher semantic consistency compared with traditional TTA generation. In addition, the reading experiments showed that the generated soundscape significantly improved the immersion experience during both silent and aloud reading. These results highlight the importance of fine-grained textual cues in cross-modal generation and support the use of structured rule-based mapping to improve the semantic alignment of TTA systems.