Benchmarking speech-to-text robustness in noisy emergency medical dialogues: an evaluation of models under realistic acoustic conditions.

ABSTRACT:

Objectives

To evaluate the transcription accuracy of 6 German-capable speech-to-text (STT) systems in simulated emergency medical services (EMS) environments, focusing on clinically relevant performance under noisy and multilingual field conditions.

Materials and methods

We generated a corpus of 99 synthetic emergency dialogues and overlaid them with ecologically valid noise types-crowd chatter, traffic, public spaces, and ambulance interiors-at 5 signal-to-noise ratios (SNRs), producing 1980 noisy audio samples. Each was transcribed by 6 STT systems (recapp, Vosk, Whisper v3 variants, and RescueSpeech). We assessed performance using 5 metrics: Word Error Rate (WER), Medical Word Error Rate (mWER), TF-IDF Cosine Similarity, BLEU, and semantic embedding similarity. Statistica

SUBMITTER: Moser D 

PROVIDER: S-EPMC12628192 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature