Language Technology
Why Maltese-English Code-Switching Breaks Generic AI Models
Ask any Maltese speaker how they talk day to day, and code-switching comes up almost immediately. A sentence might start in Maltese and finish in English, or vice versa, often mid-thought and without any signal that a switch happened.
Generic multilingual speech models are typically trained to pick one dominant language per utterance — the exact assumption code-switched Maltese speech breaks.
Why this trips up generic models
Most commercial speech-to-text systems are built for monolingual utterances. They pick the most probable language for the whole clip, then transcribe everything through that one lens.
That works fine for a sentence entirely in English or entirely in Maltese. It breaks down the moment a speaker drops in an English noun mid-Maltese-sentence, which is common in:
- Customer service calls (“I need irrid nibdel l-appuntment”)
- WhatsApp messages to a business
- Government service counters
- Everyday retail conversations
What actually goes wrong
When a generic model misjudges the dominant language, it tends to do one of two things: force the whole clip into one language’s phoneme set, or silently drop the word it can’t place. Both produce a transcript that reads wrong to a human, even if the audio was clear.
How NeuroMaltese handles it differently
NeuroMaltese is trained on real Maltese speech data, including the code-switched patterns that dominate everyday conversation, not a multilingual model with Maltese added as an afterthought. See the technology page for the benchmark detail, or the translation page for how the same handling extends to document and chatbot text.
| Approach | Handles code-switching | Trained on Maltese-specific data |
|---|---|---|
| Generic multilingual STT | Rarely | No |
| Maltese-tuned dialect model | Sometimes | Partial |
| NeuroMaltese | Yes | Yes |
If you’re evaluating options for a bilingual phone line or chatbot, talk to us about a benchmark on your own call recordings.
