DGSNA: prompt-based Dynamic Generative Scene-based Noise Addition method

To ensure the reliable operation of speech systems across diverse environments, noise addition methods have emerged as the prevailing solution. However, existing methods offer limited coverage of real-world noisy scenes and depend on pre-existing scene-based information and noise. This paper presents prompt-based Dynamic Generative Scene-based Noise Addition (DGSNA), a novel noise addition methodology that integrates Dynamic Generation of Scene-based Information (DGSI) with Scene-based Noise Addition for Speech (SNAS). This integration facilitates automated scene-based noise addition by transforming clean speech into various noise environments, thereby providing a more comprehensive and realistic simulation of diverse noise conditions. Experimental results demonstrate that DGSNA significantly enhances the robustness of speech recognition and keyword spotting models across various noise conditions, achieving a relative improvement of up to 11.21%. Furthermore, DGSNA can be effectively integrated with other noise addition methods to enhance performance. Our implementation and demonstrations are available at https://dgsna.github.io.