Dr. Elliot Moore II received his bachelor's, master's, and Ph.D. degrees in electrical and computer engineering from the Georgia Institute of Technology in 1998, 1999, and 2003, respectively. His thesis work pertained to the study and application of speech analysis techniques in the classification and recognition of stress and emotion. In particular, his dissertation presented an in-depth analysis of objectively measurable features of speech that were useable in creating patterns of separation between normal voices and those exhibiting the emotional disorder of clinical depression. After working in a post-doctorate position for about a year, Dr. Moore joined Georgia Tech as an assistant professor in the fall of 2004. He continues his research in using digital speech processing theory and analysis in the classification of human vocal patterns for determining speaker demographics (i.e., dialect, language, etc.), speaker characteristics (i.e., gender, dimensions, etc.), and speaker state (i.e., emotion, stress, etc.).
Dr. Moore’s primary research interests are in finding objective markers in speech that can be used to characterize the human condition. His interests go beyond merely identifying particular speakers (e.g., speaker identification) or determining language content (e.g., speech recognition) and rather involve the analysis of the speakers themselves. The current focus of his research centers around the analysis of vocal affect (i.e., emotion or stress) in the voice as it relates to the overall mental state of the speaker. He has done work on analyzing the effectiveness of objective speech features as indicators of clinical depression and is continuing to explore other types of emotional disorders and types of affective expression. A closely related topic to the analysis of objective speech features in Dr. Moore’s work is the extraction and analysis of the voice source (i.e., glottal waveform). Funding provided by an NSF CAREER grant has allowed him to investigate methods for voice source extraction and integration into speaker analysis. Glottal source measures are rarely included in speech analysis studies due to the difficulty in obtaining estimations from the acoustic speech waveform. The creation of robust glottal extraction algorithms for incorporation into all manners of speech analysis research is a major area of interest in Dr. Moore’s work. Dr. Moore is a member of IEEE’s Signal Processing Society and Engineering and Medicine and Biology society. He is also a member of the Acoustical Society of America.