The complex human motor function of speech presents a scientifically interesting, yet relatively unexplored, means to study brain-behavior relationships. Fortunately, magnetic resonance imaging (MRI), which has been proven to characterize soft tissue excellently, has recently become a promising technique for the study of speech. MRI's contributions in speech research could lead to new and individualized treatment for speech disorders.

Although many studies have shown that MRI can capture information about speech, this project sought to determine what covert information could be disclosed from MRI movies through multivariate analysis. The articulation of phoneme pairs was imaged using a novel sequence, and simultaneously recorded. The data were then analyzed using support vector machine (SVM) analysis and canonical correlation analysis (CCA).

Determination of classification accuracy through SVM analysis revealed that phoneme pairs were distinguishable from one another consistently over 90% of the time using information found from MRI movie clips of the speech. Additionally, study of the SVM weights demonstrated that SVM could identify regions of the vocal tract that are used to form auditory distinctions between the phonemes. Finally, CCA revealed relationships between images and the frequencies in corresponding audio waveforms; once again, the speech articulators were identified as lending maximum correlation to the sound profile.

These promising results demonstrate that multivariate analysis can uncover information that is known to be true concerning speech production. These analyses may perhaps even contribute to existing knowledge and thus provide a platform from which to advance the treatment of speech dysfunction.