Cross-Dataset, Age, and Gender Generalization: A Comprehensive Analysis of Fine-Tuning Strategies for Low-Resource Children's ASR
arXiv:2606.19791v1 Announce Type: cross Abstract: The challenge associated with recognizing dysarthric speech primarily arises from pronounced acoustic variability attributed to impaired articulatory precision. Past research has demonstrated improved recognition through the use of hybrid DNN/HMM sequence discriminative training. This paper presents a comprehensive investigation of various combinations of acoustic features tailored to different Acoustic Models, offering suitable feature selections for each. The incorporation of Pitch features notably improved recognition performance, especially
Ongoing research in AI aims to improve speech recognition for diverse, challenging audio, pushing boundaries beyond typical adult datasets.
Improving ASR for low-resource demographics like children addresses significant accessibility and usability gaps in AI applications, expanding market reach and utility.
This research provides specific architectural and feature insights for enhancing ASR performance in acoustically varied and low-resource scenarios, especially for children.
- · AI developers focused on accessibility
- · Ed-tech companies
- · Pediatric healthcare platforms
- · Speech recognition software providers
- · ASR systems lacking fine-tuning capabilities
- · Generic voice AI platforms
- · Companies ignoring niche speech recognition challenges
Improved voice interface usability for children and individuals with speech impediments.
Expansion of AI applications in education, therapy, and assistive technologies for younger populations.
Enhanced data collection and model training for underrepresented demographic groups, leading to more inclusive AI systems globally.
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Read at arXiv cs.AI