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Between 15% to 40% of mild traumatic brain injury (mTBI) patients experience incomplete recoveries or provide subjective reports of decreased motor abilities, despite a clinically-determined complete recovery. This demonstrates a need for objective measures capable of detecting subclinical residual mTBI, particularly in return-to-duty decisions for warfighters and return-to-play decisions for athletes. In this paper, we utilize features from recordings of directed speech and gait tasks completed by ten healthy controls and eleven subjects with lingering subclinical impairments from an mTBI. We hypothesize that decreased coordination and precision during fine motor movements governing speech production (articulation, phonation, and respiration), as well as during gross motor movements governing gait, can be effective indicators of subclinical mTBI. Decreases in coordination are measured from correlations of vocal acoustic feature time series and torso acceleration time series. We apply eigenspectra derived from these correlations to machine learning models to discriminate between the two subject groups. The fusion of correlation features derived from acoustic and gait time series achieve an AUC of 0.98. This highlights the potential of using the combination of vocal acoustic features from speech tasks and torso acceleration during a simple gait task as a rapid screening tool for subclinical mTBI.1