Editing State-of-the-art (section)

=== Synthesis ===
In summary, these articles investigate various approaches to enhancing Automatic Speech Recognition (ASR) systems, particularly focusing on low-resource languages, accent variations and model compression. SpecAugment demonstrates effectiveness in speaker verification tasks across different languages, while Google USM explores leveraging unlabelled data for multilingual ASR. Additionally, data augmentation techniques are shown to mitigate biases against non-native accents in Flemish ASR systems.  Self-supervised speech models like wav2vec 2.0 and adapter transfer techniques are also explored to leverage unlabeled data and efficiently adapt pre-trained models. These findings collectively underscore the importance of robust and inclusive ASR technology for diverse linguistic contexts, prompting further exploration into tailored augmentation strategies and multilingual model development to address the challenges of low-resource languages and accent diversity. The combination of transfer learning and data augmentation have also shown potential for improving ASR performance when only limited data is available, by leveraging knowledge from higher-resource languages or domains.