In this paper we describe our recent efforts to improve acousticphonetic modeling by developing sets of heterogeneous, phoneclass-specific measurements, and combining these diverse measurements into a probabilistic classification framework. We first describe a baseline classifier using homogeneous measurements. After comparing selected sub-tasks to known human performance, we define sets of phone-class-specific measurements which improve within-class classification performance. Subsequently, we combine these heterogeneous measurements into an overall context-independent classification framework. We report on a series of phonetic classification experiments using the TIMIT acoustic-phonetic corpus. Our overall framework achieves 79.0% accuracy on the NIST core test set.