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
this https URL

通过使用多语言文本编码器编码文本数据，结合一致集成蒸馏优化音频编码器，我们的方法在英语语音文本检索方面表现出最先进的性能，并且仅使用额外的 10％的语言增强训练数据，也能有效检索其他七种语言的内容。