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Evaluating pain in speech represents a critical challenge in high-stakes clinical scenarios, from analgesia delivery to emergency triage. Clinicians have predominantly relied on direct verbal communication of pain which is difficult for patients with communication barriers, such as those affected by stroke, autism, and learning difficulties. Many previous efforts have focused on multimodal data which does not suit all clinical applications. Our work is the first to collect a new English speech dataset wherein we have induced acute pain in adults using a cold pressor task protocol and recorded subjects reading sentences out loud. We report pain discrimination performance as F1 scores from binary (pain vs. no pain) and three-class (mild, moderate, severe) prediction tasks, and support our results with explainable feature analysis. Our work is a step towards providing medical decision support for pain evaluation from speech to improve care across diverse and remote healthcare settings.