Transcribing Children's Speech: ASR Performance and Obtaining Reliable Orthographic Transcriptions
arXiv:2605.28833v1 Announce Type: cross Abstract: Automatic speech recognition (ASR) has the potential to substantially reduce manual annotation effort in child speech research by generating automatic transcriptions. However, obtaining reliably high-quality ASR transcriptions for child speech remains challenging in low-resource languages due to limited child-specific pre-trained models and highly diverse noise conditions. This study investigates the effectiveness of state-of-the-art ASR models on child speech through two research questions, by evaluating nine ASR models from three model famili
Advances in AI research, particularly in speech recognition, are continuously pushing the boundaries of what's possible, making nuanced applications like child speech transcription a current focal point.
Improving ASR for child speech enables broader research into child development, education, and health, potentially impacting diagnostic tools and learning methodologies significantly.
The ability to accurately transcribe child speech shifts the resource allocation from manual annotation towards leveraging automated systems, accelerating research in this domain.
- · AI researchers (linguistics/child development)
- · Educational technology companies
- · Healthcare providers (pediatrics)
- · Speech therapy services
- · Manual transcription services (specialized in child speech)
- · Researchers relying on labor-intensive data collection
Reduced manual effort in transcribing child speech data for research and applications.
Accelerated development of AI models and tools tailored for child language development and early intervention.
Personalized educational and diagnostic tools for children become more accessible and effective globally, especially in low-resource language environments.
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Read at arXiv cs.AI