Editing State-of-the-art (section)

==== Patel, T., & Scharenborg, O. (2024). Improving End-to-End Models for Children’s Speech Recognition. ''Applied Sciences'', ''14''(6), 2353. ====
* '''Summary:''' Children’s Speech Recognition (CSR) is challenging due to variable speech patterns and limited annotated data. We aim to enhance CSR when no child speech data is available. Traditionally, Vocal Tract Length Normalization (VTLN) mitigates acoustic mismatch in hybrid systems, while End-to-End (E2E) systems use data augmentation. We investigate speed perturbations, spectral augmentation, and VTLN in E2E CSR systems across Dutch, German, and Mandarin. Our experiments show that speed perturbations and spectral augmentation significantly improve performance, with VTLN offering further enhancements while maintaining adult speech recognition. VTLN benefits both genders and is particularly effective for younger children.
* '''RQ:''' How to enhance SCR performance while maintaining performance on adults’ speech when adapting the model to children’s speech?
* '''Hypothesis:''' VLTN, speed perturbation, and spectral augmentation can be useful.
* '''Conclusion:''' VLTN is used for the 1st times to improve E2E CSR work  augmentation and normalization enhance CSR task performance  the performance of adult speech is largely preserved  similar observations in all 3 languages
* '''Critical observations:''' Because VTLN needs to be trained independently and then used as a processing step after feature extraction to warp the features for training the ASR network architecture, it may not be compatible with architectures that utilize raw waveform data rather than features. As a result, integrating VTLN into such architectures requires further exploration.
* '''Relevance:''' The study's focus on improving Automatic Speech Recognition (ASR) for children's speech, despite limited annotated data, holds relevance to the endeavor of enhancing ASR performance for older adults. Both populations present challenges due to variability in speech patterns and the scarcity of annotated data. Techniques explored in the study, such as Vocal Tract Length Normalization (VTLN) and data augmentation, offer potential solutions that could be adapted to address age-related changes in older adults' speech. Comparative analyses across languages and considerations of age and gender factors provide valuable insights applicable to developing tailored ASR systems for the older adult population. Overall, the study's methodologies and findings offer valuable parallels and considerations for researchers aiming to improve ASR performance for older adults.