An important, yet under-explored, problem in speech processing is the automatic assessment of intelligibility for pathological speech. In practice, intelligibility assessment is often done through subjective tests administered by speech pathologists; however research has shown that these tests are inconsistent, costly, and exhibit poor reliability. Although some automatic methods for intelligibility assessment for telecommunications exist, research specific to pathological speech has been limited. Here, we propose an algorithm that captures important multi-scale perceptual cues shown to correlate well with intelligibility. Nonlinear classifiers are trained at each time scale and a final intelligibility decision is made using ensemble learning methods from machine learning. Preliminary results indicate a marked improvement in intelligibility assessment over published baseline results.
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