Audio Captioning using Pre-Trained Large-Scale Language Model Guided by Audio-based Similar Caption Retrieval

TL;DR

This paper introduces a novel audio captioning approach that leverages pre-trained large-scale language models guided by similar caption retrieval to improve description generation despite limited training data.

Contribution

It proposes a new method combining pre-trained language models with guidance from retrieved similar captions, addressing data scarcity in audio captioning.

Findings

Pre-trained language models can be effectively used for audio captioning.

Guidance captions improve caption generation quality.

Oracle performance surpasses traditional training methods.

Abstract

The goal of audio captioning is to translate input audio into its description using natural language. One of the problems in audio captioning is the lack of training data due to the difficulty in collecting audio-caption pairs by crawling the web. In this study, to overcome this problem, we propose to use a pre-trained large-scale language model. Since an audio input cannot be directly inputted into such a language model, we utilize guidance captions retrieved from a training dataset based on similarities that may exist in different audio. Then, the caption of the audio input is generated by using a pre-trained language model while referring to the guidance captions. Experimental results show that (i) the proposed method has succeeded to use a pre-trained language model for audio captioning, and (ii) the oracle performance of the pre-trained model-based caption generator was clearly better than that of the conventional method trained from scratch.