Editing State-of-the-art (section)

==== Wang, C., Wu, Y., Du, Y., Li, J., Liu, S., Lu, L., ... & Zhou, M. (2020). Semantic mask for transformer based end-to-end speech recognition. arXiv preprint arXiv:1912.03010. ====

* '''Summary''': The article presents a semantic mask-based augmentation approach for improving end-to-end  ASR systems. This method involves masking the input features corresponding to specific output tokens, such as words or word-pieces, during training (similar to how BERT is trained with its [MASK] token). The objective is to force the model to predict the masked tokens using contextual information, with this enhancing the model's generalization capabilities. Experiments on the Librispeech 960h and TedLium2 datasets demonstrated state-of-the-art performance, showing the effectiveness of this approach. 
* '''RQ''': Can the generalization capacity and language modeling power of end-to-end ASR models be improved with the employment of an NLP technique of semantic masking?
* '''Hypothesis''': By applying a semantic mask to mask out input features corresponding to specific output tokens, the models will be encouraged to rely more on contextual information, improving their modeling capabilities and generalization.
* '''Conclusion''': The introduction of a semantic mask in transformer-based E2E ASR models leads to significant improvements in WER on the Librispeech and TedLium2 datasets. This approach enhances the model's ability to use contextual information and strenghtens its robustness to various acoustic distortions, which potentially can be useful for the task of whispering speech recognition as well.
* '''Critical observations''': The semantic mask approach is particularly effective in challenging conditions, where reliance on contextual information becomes crucial for accurate token prediction, so I may assume it could prove useful in whispering speech too, where one word could be more prominent than the other. However, while the paper describes the semantic masking strategy, further details on how tokens could be selected for masking and the criteria for that could enhance reproducibility and allow for more detaield analysis of why this strategy works.
* '''Relevance''': Semantic Masking's emphasis on enhancing a model's reliance on contextual information rather than solely on acoustic features suggests that it could be relevant for whispering speech recognition. Whispered speech which is characterized by reduced dynamic range and spectral variations, presnts unique challenges that, I guess, might be mitigated by a model better attuned to contextual cues, where one part of the utterance might be more prominent than the other.