Language Identification

Language identification studies how to infer a text’s or speech segment’s language, from whole documents and recordings down to individual words in code-switched conversations. Common approaches include character and word-based classifiers, contextual multilingual models, tokenizer likelihoods, and speech systems built on pretrained encoders; some methods also adjust representations to reduce accent-related errors. Evaluation spans multilingual benchmarks, regional varieties, noisy or transliterated text, mixed-language social media, and speech recorded across different acoustic conditions. Systems can perform well on familiar data, but low-resource languages, closely related varieties, code-switch boundaries, and shifts in speaker or recording conditions remain difficult, and benchmark results do not always predict real-world reliability.