Braille-to-Speech Generator: Audio Generation Based on Joint Fine-Tuning of CLIP and Fastspeech2

TL;DR

This paper presents a Chinese Braille-to-speech framework using joint fine-tuning of CLIP and Fastspeech2, improving speech synthesis quality and efficiency for visually impaired users with limited data.

Contribution

It introduces a novel Chinese Braille-to-speech model with joint fine-tuning of CLIP and Fastspeech2, addressing data limitations and language applicability issues.

Findings

Improved BLEU4, FAD, WER metrics on multiple datasets.

High-quality speech synthesis with limited data.

Effective joint training strategy validated.

Abstract

An increasing number of Chinese people are troubled by different degrees of visual impairment, which has made the modal conversion between a single image or video frame in the visual field and the audio expressing the same information a research hotspot. Deep learning technologies such as OCR+Vocoder and Im2Wav enable English audio synthesis or image-to-sound matching in a self-supervised manner. However, the audio data used for training is limited and English is not universal for visually impaired people with different educational levels. Therefore, for the sake of solving the problems of data volume and language applicability to improve the reading efficiency of visually impaired people, a set of image-to-speech framework CLIP-KNN-Fastspeech2 based on the Chinese context was constructed. The framework integrates multiple basic models and adopts the strategy of independent pre-training and joint fine-tuning. First, the Chinese CLIP and Fastspeech2 text-to-speech models were pre-trained on two public datasets, MUGE and Baker, respectively, and their convergence was verified. Subsequently, joint fine-tuning was performed using a self-built Braille image dataset. Experimental results on multiple public datasets such as VGGSound, Flickr8k, ImageHear, and the self-built Braille dataset BIT-DP show that the model has improved objective indicators such as BLEU4,FAD(Fr\'echet Audio Distance), WER(Word Error Ratio), and even inference speed. This verifies that the constructed model still has the ability to synthesize high-quality speech under limited data, and also proves the effectiveness of the joint training strategy that integrates multiple basic models.