Recovering clean speech from an audio signal with additive noise is a problem that has plagued the signal processing community for decades. One promising technique currently being utilized in speech-coding applications is a multi-sensor approach, in which a microphone is used in conjunction with optical, mechanical, and electrical non-acoustic speech sensors to provide greater versatility in signal processing algorithms. One such non-acoustic glottal waveform sensor is the Tuned Electromagnetic Resonator Collar (TERC) sensor, first developed in [BLP+02]. The sensor is based on Magnetic Resonance Imaging (MRI) concepts, and is designed to detect small changes in capacitance caused by changes to the state of the vocal cords - the glottal waveform. Although preliminary simulations in [BLP+02] have validated the basic theory governing the TERC sensor's operation, results from human subject testing are necessary to accurately characterize the sensor's performance in practice. To this end, a system was designed and developed to provide real-time audio recordings from the sensor while attached to a human test subject. From these recordings, executed in a variety of acoustic noise environments, the practical functionality of the TERC sensor was demonstrated. The sensor in its current evolution is able to detect a periodic waveform during voiced speech, with two clear harmonics and a fundamental frequency equal to that of the speech it is detecting. This waveform is representative of the glottal waveform, with little or no articulation as initially hypothesized. Though statistically significant conclusions about the sensor's immunity to environmental noise are difficult to draw, the results suggest that the TERC sensor is considerably more resistant to the effects of noise than typical acoustic sensors, making it a valuable addition to the multi-sensor speech processing approach.