Integration of engineering, linguistics and speech science in speech communication; speech recognition based on phonetic features to address the limitations of present recognizers (e.g., speaker dependence); speech production; speech enhancement.
Carol Espy-Wilson is a Professor in the Electrical and Computer Engineering Department and the Institute for Systems Research at the University of Maryland.
Dr. Espy-Wilson received a B.S. in Electrical Engineering from Stanford University in 1979, and a M.S., E.E. and Ph.D. in Electrical Engineering from the Massachusetts Institute of Technology in 1981, 1984 and 1987, respectively. Prior to joining the faculty at the University of Maryland, Dr. Espy-Wilson was a faculty member at Boston University.
Dr. Espy-Wilson's research is in speech communication. She combines knowledge of digital signal processing, speech science, linguistics and acoustic phonetics to conduct interdisciplinary research in speech recognition, speech production and speaker recognition. Specific research projects include (a) the development of a speech signal representation that contains only linguistic information, (b) the development of a speech signal representation that highlights speaker characteristics, (c) the development of a probabilistic framework for an event-based speech recognition system, (d) the development of supervised and unsupervised acoustic models for speaker recognition and (e) the development of vocal tract models of complex speech sounds.
She also conducts research in the areas of speech production, speech enhancement, speaker recognition, single-channel speaker separation and Language and Genre detection in Audio Content Analysis.
Dr. Espy-Wilson has authored or coauthored numerous papers in journals, conference proceedings and books. She is a Fellow of the Acoustical Society of America (ASA) and a Senior Member of IEEE. She was a Radcliffe Fellow at Harvard 2008–2009. Among the other honors and awards she has received for her research contributions are the Clare Boothe Luce Professorship in 1990, the Independent Scientist Award from the National Institutes of Health in 1998 and the Honda Initiation Award in 2003. She served as the chair of the Speech Technical Committee of the Acoustical Society of America (2007-2010) and as an Associate Editor of the ASA's magazine, Acoustics Today. Currently, she is an Associate Editor of the Journal of the Acoustical Society of America and she is a member of the National Advisory Board for Medical Rehabilitation at the National Institutes of Health.
Honors and Awards
Acoustical Society of America, 2005
Other professional awards
NIH Independent Scientist Award
Grand Prize, Rockville Economic Development Inc. (REDI) StartRight! Women’s Business Plan Competition, 2010
$50,000 SAIC-VentureAccelerator Competition, 2010
Maryland Innovator of the Year, 2010
University of Maryland awards
University of Maryland Distinguished Scholar-Teacher Award, 2012
University of Maryland $75K Business Plan Competition (High Technology), 2010
Invention of the Year (Information Science): OmniSpeech, 2010
- Multilingual Gestural Models for Robust Language-Independent Speech Recognition
- Speech Processing Algorithms for Elderly Listeners with Hearing Loss
- Nonintrusive Digital Speech Forensics: Source Identification and Content Authentication
- Predictors of Speech Quality after Tongue Cancer Surgery
- Advanced speech enhancement software
- An Enhancement of Modified Phase Opponency for Noise-Robust Speech Recognition
- Acoustic-phonetic Approach to Speech Recognition Based on Landmark Detection
- Speech Segregation from Co-Channel Mixtures
- Multi-faceted research in Speech Communications
- From Acoustics to Vocal-Tract Time Functions
- Speech Enhancement for Noise—Robust Speech Recognition
- Language Detection for Music Information Retrieval
- Acoustic Parameters for Automatic Detection of Nasal Manner
- Landmark-Based Speech Recognition
- A Novel Speaker Verification System using Samples-Based Acoustical Models
- Synergy of Acoustic-Phonetics and Peripheral Auditory Modeling Towards Robust Speech Recognition