Can Voices Predict Emergency Severity? An Exploratory Analysis of EMS Calls

Abstract: Emergency medical services (EMS) rely heavily on verbal communication during the initial phase of response. Dispatchers are trained to assess urgency based on both the content and tone of callers' voices. However, the potential to objectively estimate patient severity based solely on acoustic features of the caller’s speech has not been fully explored. This study investigates the feasibility of classifying patient severity—specifically distinguishing between "critical" and "non-critical" cases—using non-linguistic features derived from emergency call audio.We utilized real-world emergency call recordings provided by the Tokyo Fire Department. Each audio sample was labeled as either “critical” or “non-critical” based on post-response evaluations by emergency personnel or medical institutions. Acoustic features were extracted from the callers’ speech, including fundamental frequency (pitch), speech rate, jitter, intensity, and mel-frequency cepstral coefficients (MFCCs). No textual content was analyzed in this study; we focused exclusively on paralinguistic and spectral aspects of speech.Using the extracted features, we trained classification models including logistic regression, support vector machines (SVM), and random forest classifiers. Feature selection and dimensionality reduction techniques, such as recursive feature elimination and principal component analysis (PCA), were applied to optimize model performance and identify key indicators. Classification performance was evaluated using standard metrics, though specific numeric results are omitted here due to the exploratory nature of the study.Our findings indicate that certain vocal characteristics—such as elevated pitch, increased speech rate, and variability in vocal intensity—were more frequently observed in calls associated with critical cases. These patterns may reflect psychological urgency, stress, or heightened emotional states in the caller, indirectly signaling the severity of the patient’s condition.This study demonstrates the potential of voice-based severity estimation as a supplementary tool for EMS dispatchers. By integrating such acoustic analysis into the triage process, emergency services may be able to support faster and more informed decision-making, especially in high-pressure environments where every second counts. The approach may also contribute to more effective resource allocation by identifying high-priority cases earlier in the response timeline.However, several limitations must be acknowledged, including the relatively limited size of the dataset, variability in recording conditions, and the influence of caller demographics and emotional disposition. Future work will aim to expand the dataset, incorporate automatic speech recognition (ASR) for combined linguistic and acoustic analysis, and explore deep learning-based classification models for improved generalization and robustness.This exploratory research provides foundational insights into the integration of AI-based acoustic analysis into emergency response workflows and highlights its potential to enhance the speed and accuracy of prehospital assessments.

Keywords: Emergency voice analysis, Severity prediction, Acoustic feature extraction

DOI: 10.54941/ahfe1006919

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