A major hurdle in computational speech analysis is the effective integration of available tools originally developed for purposes unrelated to each other. We present a Python-based tool to enable an efficient and organized processing workflow incorporating automatic speech recognition using HTK, phoneme-level prosodic feature extraction in Praat and machine learning in WEKA. Our system is extensible, customizable and organizes prosodic data by phoneme and time stamp in a tabular fashion in preparation for analysis using other utilities. Plotting of prosodic information is supported to enable visualization of prosodic features.