SPEech Feature Toolbox (SPEFT) Design and Emotional Speech Feature Extraction

Preface This research focuses on designing the SPEech Feature Toolbox (SPEFT), a toolbox which integrates a large number of speech features into one graphic interface in the MATLAB environment. The toolbox is designed with a Graphical User Interface (GUI) interface which makes it easy to operate; it also provides batch process capability. Available features are categorized into subgroups including spectral features, pitch frequency detection, formant detection, pitch related features and other time domain features. A speaking style classification experiment is carried out to demonstrate the use of the SPEFT toolbox, and validate the usefulness of non-traditional features in classifying different speaking styles. The pitch-related features jitter and shimmer are combined with the traditional spectral and energy features MFCC and log energy. A Hidden Markov Models (HMMs) classifier is applied to these combined feature vectors, and the classification results between different feature combinations are compared. A thorough test of the SPEFT toolbox is also presented by comparing the extracted feature results between SPEFT and previous toolboxes across a validation test set. Acknowledgements I thank my advisor, Dr. Michael Johnson for giving me this precious opportunity to work in his speech group under NSF's Dr. Do-little project, and for the generous help and insightful guidance he has offered me in the past two years. I thank my master's thesis committee for their support and helpful reviews, Dr. I thank my colleagues, Marek, Yao, Jidong and Patrick, who constantly and generously shared their knowledge in their research fields. Most importantly, I would like to express gratitude to my parents and family members, who have been the inspiration throughout this journey. Finally, I thank all the collaborators under Dr. Do-little project, the well labeled data and their time-cosuming work significantly facilitated the process this research.