Parameter Efficient Audio Captioning With Faithful Guidance Using Audio-text Shared Latent Representation
This work addresses deployment challenges for audio captioning on edge devices by reducing model size and improving faithfulness, though it is incremental in its approach.
The paper tackles the problems of hallucination and large memory footprint in audio captioning models by proposing a parameter-efficient inference-time faithful decoding algorithm that uses audio-text shared latent representations to align generated text with input audio, achieving performance equivalent to larger models while reducing complexity.
There has been significant research on developing pretrained transformer architectures for multimodal-to-text generation tasks. Albeit performance improvements, such models are frequently overparameterized, hence suffer from hallucination and large memory footprint making them challenging to deploy on edge devices. In this paper, we address both these issues for the application of automated audio captioning. First, we propose a data augmentation technique for generating hallucinated audio captions and show that similarity based on an audio-text shared latent space is suitable for detecting hallucination. Then, we propose a parameter efficient inference time faithful decoding algorithm that enables smaller audio captioning models with performance equivalent to larger models trained with more data. During the beam decoding step, the smaller model utilizes an audio-text shared latent representation to semantically align the generated text with corresponding input audio. Faithful guidance is introduced into the beam probability by incorporating the cosine similarity between latent representation projections of greedy rolled out intermediate beams and audio clip. We show the efficacy of our algorithm on benchmark datasets and evaluate the proposed scheme against baselines using conventional audio captioning and semantic similarity metrics while illustrating tradeoffs between performance and complexity.