Editing State-of-the-art (section)

==== Zhang, Wangyou, and Yanmin Qian. “Weakly-Supervised Speech Pre-Training: A Case Study on Target Speech Recognition.” arXiv, June 29, 2023. <nowiki>http://arxiv.org/abs/2305.16286</nowiki>. ====
* Summary: This study introduces a new way to teach computers to understand speech by focusing on one person's voice in a noisy place, like when many people talk at once. This method, called TS-HuBERT, uses extra information about the speaker's voice to improve speech recognition, especially in challenging situations with lots of background noise. Tests showed that TS-HuBERT does a better job than other similar methods, making it a promising approach for better understanding speech in noisy environments.
* RQ: Can we use extra information about who is speaking to help computers better recognize speech in noisy settings?
* Hypothesis: By using additional information about the speaker, the TS-HuBERT method can focus on the target speaker's voice more effectively, even when other voices or noises are present.
* Conclusion: TS-HuBERT improves speech recognition by focusing on the target speaker's voice, outperforming other current methods. This approach is particularly useful for recognizing speech in noisy places where many people are talking at once.
* Critical observations: 
** TS-HuBERT can be adjusted to different speech recognition tasks, showing its versatility.
** Although it needs extra information about the speaker's voice, this method greatly enhances the computer's ability to focus on and understand the target speaker in noisy situations.
** There is still room for improvement, especially in very noisy environments, indicating potential areas for future research.
* Relevance: This study is directly relevant to the topic to help computers understand speech better in challenging environments, like when many people are talking at the same time. By focusing on a specific speaker's voice, TS-HuBERT could make speech recognition technology more effective in real-world situations.