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, phonemelevel 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.
|Original language||English (US)|
|Number of pages||2|
|Journal||Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH|
|State||Published - Dec 1 2011|
|Event||12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011 - Florence, Italy|
Duration: Aug 27 2011 → Aug 31 2011