Your language filter can't see a sister language: data lessons from a low-resource build

Building speech and language models for low-resource languages like Crimean Tatar requires meticulous data curation, as readily available text and audio often present significant challenges. Key lessons learned include conducting a thorough inventory of existing materials before committing to expensive processing pipelines and recognizing that 'free text' is often mechanically broken due to encoding issues, duplicate content, or incorrect character substitutions. Careful validation of repairs on known-good data is essential before applying them to the entire corpus.

Read the full article at DEV Community

---

Want to create content about this topic? Use Nemati AI tools to generate articles, social posts, and more.