Current models of human vocal production that capture peripheral dynamics in speech require large dimensional measurements of the neural activity, which are mapped into equally complex motor gestures. In this work we present a motor description for vowels as points in a discrete low-dimensional space. We monitor the dynamics of 3 points at the oral cavity using Hall-effect transducers and magnets, describing the resulting signals during normal utterances in terms of active/inactive patterns that allow a robust vowel classification in an abstract binary space. We use simple matrix algebra to link this representation to the anatomy of the vocal tract and to recent reports of highly tuned neuronal activations for vowel production, suggesting a plausible global strategy for vowel codification and motor production.