arXiv:2606.16019v1 Announce Type: new Abstract: Expert phonetic annotation is costly, especially for non-standard dialects and atypical speech. A common alternative is using Grapheme-to-Phoneme (G2P) models to auto-generate phonetic labels from text transcripts at scale. We study how automatic phonetic transcription performance scales with human and G2P supervision in English. Using a curated 80-hour benchmark spanning native, non-native and post-stroke speech, we identify a supervision quality threshold: G2P supervision helps only when fewer than 20-30 hours of human annotation are available.
The proliferation of AI models for speech and language processing necessitates more efficient and scalable annotation methods, driving research into optimal human-G2P supervision strategies.
This research provides a concrete threshold for when automated G2P models become more cost-effective than human annotation, directly impacting development timelines and resource allocation for AI speech systems.
The understanding of how to efficiently bootstrap and scale phonetic transcription for diverse speech, shifting resource allocation towards G2P for smaller datasets and human experts for larger, more critical ones.
- · AI speech development teams
- · NLP researchers relying on phonetic data
- · Companies working with non-standard dialects or atypical speech
- · Human phonetic annotators for small datasets
Reduced cost and time for developing speech recognition and synthesis systems in under-resourced languages and dialects.
Faster deployment of AI language technologies to a wider range of global users and specialized medical applications.
Potentially democratizes access to sophisticated speech AI, fostering innovation outside of major language markets and increasing accessibility for individuals with speech impediments.
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