Code-switching: the data problem nobody scopes for
Most of the world mixes languages mid-sentence. If your dataset treats that as noise, it will not match how people actually speak.
A bilingual speaker in Manila says a sentence that starts in Tagalog, switches to English for a technical term, and returns to Tagalog. A Hindi speaker in Delhi does the same with English. A Spanish speaker in Texas does it with Spanish and English in nearly every conversation.
This is code-switching, and for a large share of the world it is not a special case. It is the normal register.
Why it breaks standard data pipelines
Most speech data projects are specified as monolingual by default. The language field says "Tagalog," so the annotators transcribe in Tagalog — and when they hit an English phrase, they either translate it, transliterate it, or drop it.
Each annotator makes that call individually. The result is a dataset where the same phenomenon is handled three different ways, and a model trained on it learns none of them reliably.
The decisions that have to be made up front
Code-switching data is not harder to collect than monolingual data. It is harder to specify, because every one of these questions has multiple reasonable answers.
- Transcription: do you write the switched-language words in their own script, or transliterate them into the matrix language?
- Boundary marking: do you label the exact word where the switch happens, or just tag the whole utterance?
- Normalization: if a speaker says an English word with a Tagalog accent, do you transcribe the English spelling or a phonetic approximation?
- Coverage: do you want natural switch density, or a balanced sample with more switching than occurs naturally?
Why the boundary question is the expensive one
Word-level switch boundaries are what make code-switching data genuinely valuable, because they let you train a model to track the switch rather than just tolerate it.
They are also the part that costs the most, because annotators disagree about them. In practice the disagreements cluster around a small set of cases — proper nouns, established loanwords, and numerals — which means a guideline that addresses those three categories explicitly can raise agreement substantially.
Where this matters most
The languages where code-switching dominates tend to be the ones with the largest speaker populations and the fastest-growing voice product markets: Tagalog, Hindi, Spanish in the Americas, Malay, Swahili, and the urban varieties of Arabic.
A model deployed to those markets that was trained only on monolingual data will degrade exactly where it is being used.