Editing State-of-the-art (section)

=== Synthesis ===
In conclusion, all these studies underscore the importance of innovative approaches in enhancing ASR systems' performance and robustness, finding the necessary tricks to solve the complexities dictated by challenging acoustic features and environments. They demonstrate the potential of data augmentation and speech separation, and push the boundaries of what's achievable in speech recognition tasks in general through focusing on very specific tasks.

Through these works, a noticeable shift from conventional RNN-based structures to Transformer models can be noticed, as evidenced by SepFormer and ConSep. These models take advantage of the ability to process sequences in parallel, resulting in significant improvements in efficiency and scalability. The use of techniques such as SpecAugment and semantic masks, in turn, highlights the increasing recognition of the importance of robust data augmentation in conditions of insufficient data. These methods improve model generalisation, enabling systems to handle a wider variety of speech inputs more effectively.

There is an ongoing debate about the relative contribution of different augmentation techniques, such as the impact of time warping versus time masking. This debate highlights the need for a better understanding of how different aspects of speech data contribute to model learning and performance. The integration of external language models with ASR systems is a also topic of separate discussion: although research has shown remarkable performance without them, the debate continues on the best approach to find and keep contextual information for speech recognition. When it comes to child speech recognition, a debate might arise around the scalability of ConSep vs SepFormer to handle larger datasets, questioning whether ConSep's specialized approach or SepFormer's more generalized framework is better suited for future advancements in ASR technology.

We think that in the future, research should focus on integrating multi-modal data and enhancing adaptation to diverse acoustic environments, and the studies reviewed are certainly a step towards at least the latter. We are sure that the combination of audio and visual data would present new opportunities for improving speech recognition in such challenging settings: for whispering speech recognition, for instance, there already exists a database called Audiovisual Whisper which audios and videos of whispering, and much work is being done in that direction. However, even though there's a lot work ahead, this short list of studies that we discussed here already shows big steps forward in speech technology, opening doors to more flexible speech recognition systems that are better suited for everyday use.