Bridging Language Gaps in Audio-Text Retrieval
<|reference_start|>Bridging Language Gaps in Audio-Text Retrieval: Audio-text retrieval is a challenging task, requiring the search for an audio clip or a text caption within a database. The predominant focus of existing research on English descriptions poses a limitation on the applicability of such models, given the abundance of non-English content in real-world data. To address these linguistic disparities, we propose a language enhancement (LE), using a multilingual text encoder (SONAR) to encode the text data with language-specific information. Additionally, we optimize the audio encoder through the application of consistent ensemble distillation (CED), enhancing support for variable-length audio-text retrieval. Our methodology excels in English audio-text retrieval, demonstrating state-of-the-art (SOTA) performance on commonly used datasets such as AudioCaps and Clotho. Simultaneously, the approach exhibits proficiency in retrieving content in seven other languages with only 10% of additional language-enhanced training data, yielding promising results. The source code is publicly available https://github.com/zyyan4/ml-clap.<|reference_end|>
@article{yan2024bridging,
    title={Bridging Language Gaps in Audio-Text Retrieval},
    author={Zhiyong Yan, Heinrich Dinkel, Yongqing Wang, Jizhong Liu, Junbo Zhang,
  Yujun Wang, Bin Wang},
    journal={arXiv preprint arXiv:2406.07012},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07012},
    primaryClass={cs.SD cs.CL eess.AS}
}