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In this paper, we present Jawthenticate, an earable system that authenticates a user using audible or inaudible speech without us- ing a microphone. This system can overcome the shortcomings of traditional voice-based authentication systems like unreliability in noisy conditions and spoofing using microphone-based replay attacks. Jawthenticate derives distinctive speech-related features from the jaw motion and associated facial vibrations. This combi- nation of features makes Jawthenticate resilient to vocal imitations as well as camera-based spoofing. We use these features to train a two-class SVM classifier for each user. Our system is invariant to the content and language of speech. In a study conducted with 41 subjects, who speak different native languages, Jawthenticate achieves a Balanced Accuracy (BAC) of 97.07%, True Positive Rate (TPR) of 97.75%, and True Negative Rate (TNR) of 96.4% with just 3 seconds of speech data.