Editing State-of-the-art (section)

==== Geng, M., Xie, X., Liu, S., Yu, J., Hu, S., Liu, X., & Meng, H. (2022). Investigation of data augmentation techniques for disordered speech recognition. ''arXiv preprint arXiv:2201.05562''. ====
* '''Summary:''' The final speaker adapted system constructed using the UASpeech corpus and the best augmentation approach based on speed perturbation produced up to 2.92% absolute (9.3% relative) word error rate (WER) reduction over the baseline system without data augmentation, and gave an overall WER of 26.37% on the test set containing 16 dysarthric speakers.
* '''RQ:''' systematically investigate different data augmentation techniques for disordered speech recognition.
* '''Conclusion:''' It suggests that speed-perturbation based augmentation produces the largest improvement in system performance despite the huge mismatch between normal and disordered speech.
* '''Critical observations:'''  They increased the amount of speed perturbation data to four times and six times, with only dysarthric speech being processed, the mean WER showed that four times the amount of the original data made the model performance better than six times (4x: 29.47, 6x: 29.52). More augmented data cannot further improve the model performance. In addition, increasing the augmented data from two to four times only reduced the WER by 0.2%. They did not further increase the amount of augmented data, while according to the results when only dysarthric speech data was augmented, it is doubtful whether more data can still lower the WER. This can be explored in future studies by increasing the amount of augmented data from one to six or more times while keeping all other factors the same.
* '''Relevance:''' The study exploring data augmentation techniques for dysarthric speech recognition offers insights applicable to improving ASR performance for older adults. By addressing challenges common to both dysarthric speech and speech from older adults, such as variations in speech patterns and articulation, the study provides valuable methodologies and findings. Specifically, the effectiveness of techniques like speed perturbation-based augmentation in enhancing ASR performance underscores their potential utility in optimizing systems for recognizing older adult speech. Furthermore, the study's identification of augmentation limitations and suggestions for future research pave the way for continued refinement of ASR systems tailored to the unique characteristics of older adult speech.