Asian Small-Clawed Otters (Aonyx cinerea) are a small, protected but threatened species living in freshwater. They are gregarious and live in monogamous pairs for their lifetimes, communicating via scent and acoustic vocalizations. This study utilized a hidden Markov model (HMM) to classify stress versus non-stress calls from a sibling pair under professional care. Vocalizations were expertly annotated by keepers into seven contextual categories. Four of these-aggression, separation anxiety, pain, and prefeeding-were identified as stressful contexts, and three of them-feeding, training, and play-were identified as non-stressful contexts. The vocalizations were segmented, manually categorized into broad vocal type call types, and analyzed to determine signal to noise ratios. From this information, vocalizations from the most common contextual categories were used to implement HMM-based automatic classification experiments, which included individual identification, stress vs non-stress, and individual context classification. Results indicate that both individual identity and stress vs non-stress were distinguishable, with accuracies above 90%, but that individual contexts within the stress category were not easily separable.