Bridging Language Gaps in Audio-Text Retrieval

Understand

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.