# Enhancing Gappy Speech Audio Signals with Generative Adversarial Networks

**Authors:** Deniss Strods, Alan F. Smeaton  
**arXiv:** [2305.05780](https://arxiv.org/abs/2305.05780)  
**Date:** 2023-05-09  
**Summary page:** https://www.summarizepaper.com/en/paper/2305.05780v1/

## Key points

- Addressing gaps, dropouts, and corrupted audio segments is crucial for improving speech signal quality
- Novel approach leverages machine learning techniques to regenerate gaps in audio speech signals up to 320ms in length
- Audio regeneration achieved by transforming audio into Mel-spectrograms and utilizing image in-painting techniques
- Complete Mel-spectrogram converted back into audio using Parallel-WaveGAN vocoder
- Study conducted experiments on a dataset of 1300 spoken audio clips from the LJSpeech dataset
- Results show that Generative Adversarial Networks (GANs) can effectively regenerate gaps in audio in close to real-time on GPU-equipped systems
- Smaller gaps result in higher quality filled gaps
- Speech enhancement is essential for improving perceptual and aesthetic aspects of degraded speech signals affected by noise
- Enhancing speech quality is vital for applications such as mobile communications, hearing aids, and robust speech recognition systems
- Research delves into related areas like GAN applications, variant architectures, and speech enhancement in noisy environments

## Summary

In the field of speech enhancement, addressing gaps, dropouts, and corrupted audio segments is crucial for improving the overall quality of speech signals. This paper introduces a novel approach that leverages machine learning techniques to regenerate gaps in audio speech signals. The focus is on gaps up to 320ms in length. By translating audio regeneration into image regeneration through the transformation of audio into Mel-spectrograms and utilizing image in-painting techniques, the gaps in the audio are effectively filled. The complete Mel-spectrogram is then converted back into audio using the Parallel-WaveGAN vocoder and seamlessly integrated into the audio stream. The study conducted experiments using a dataset of 1300 spoken audio clips from the LJSpeech dataset. Results show that with the use of Generative Adversarial Networks (GANs) on a GPU-equipped system, gaps in audio can be effectively regenerated in close to real-time. Smaller gaps lead to higher quality filled gaps. This paper also delves into related research areas such as GAN applications and variant architectures, as well as speech enhancement in noisy environments. Speech enhancement plays a vital role in improving both perceptual and aesthetic aspects of degraded speech signals affected by noise. The task of enhancing speech quality is essential for various applications including mobile communications, hearing aids, and robust speech recognition systems. Overall, this work sheds light on innovative methods for enhancing gappy speech audio signals using advanced machine learning techniques like GANs. By addressing gaps and dropouts effectively, this research contributes towards improving overall communication experiences and advancing speech processing technologies.