Automatic Speech Recognition for Crisis Communication in the Albanian Language: Evaluating Whisper Turbo

This study evaluates the performance of the Whisper Turbo automatic speech recognition (ASR) model for crisis communication in the Albanian language. Applying a robust system such as Whisper Turbo will be very challenging because the stress and urgency of speaking in emergency situations will bring a rapid tempo and emotional intonation. We assess its accuracy and speed on two distinct datasets: a controlled corpus of formal, literary Albanian and a challenging corpus of emergency-style speech from first responders. The research uses word error rate (WER) and character error rate (CER) to quantify performance. We found a significant performance difference between the two conditions. The model achieved an average WER of 52.9% on formal Albanian but degraded to 63.5% on the dialectal and stressed speech. These results indicate a clear bias toward standardized language and highlight the model’s difficulty with non-standard pronunciations, emotional intonation, and specialized vocabulary. The findings underscore that while current multilingual ASR models can process low-resource languages such as Albanian, they are not yet suitable for deployment in critical emergency contexts without domain-specific fine-tuning. This work contributes an essential evaluation to the under-researched field of Albanian ASR and provides a foundation for developing more robust and reliable systems for crisis communication.