Hansen, John H. L.
Browse by
John Hansen holds the Distinguished University Endowed Chair in Telecommunications Engineering. He serves as Associate Dean for Research and Professor of Electrical Engineering in the Erik Jonsson School of Engineering and Computer Science. He also founded UTD's Center for Robust Speech Systems. Finally, he holds a professorship in the School of Behavorial and Brain Sciences focusing on speech and hearing.
Dr. Hansen's expertise includes the areas of:
- Voice recognition
- Speaker recognition
- Speaker analysis
- Speech modeling
- Smart cars
- Driving behavior
- Digital speech processing
- Speech forensics
- Speech enhancement
- Human-computer interaction
- Speech analysis
Learn more about Dean Hansen's work on his Endowed Professorships and Chairs, Home and Research Explorer pages.
Works in Treasures @ UT Dallas are made available exclusively for educational purposes such as research or instruction. Literary rights, including copyright for published works held by the creator(s) or their heirs, or other third parties may apply. All rights are reserved unless otherwise indicated by the copyright owner(s).
Recent Submissions
-
Speech and Language Processing for Assessing Child–Adult Interaction Based on Diarization and Location
(Springer New York LLC, 2019-06-05)Understanding and assessing child verbal communication patterns is critical in facilitating effective language development. Typically speaker diarization is performed to explore children’s verbal engagement. Understanding ... -
Enhancement of Consonant Recognition in Bimodal and Normal Hearing Listeners
(Sage Publications Inc., 2019-05-15)Objectives: The present study investigated the effects of 3-dimensional deep search (3DDS) signal processing on the enhancement of consonant perception in bimodal and normal hearing listeners. Methods: Using an articulation-index ... -
Leveraging Frequency-Dependent Kernel and Dip-Based Clustering for Robust Speech Activity Detection in Naturalistic Audio Streams
Speech activity detection (SAD) is front-end in most speech systems e.g. speaker verification, speech recognition etc. Supervised SAD typically leverages machine learning models trained on annotated data. For applications ... -
Speech Enhancement for Cochlear Implant Recipients.
In this study, a single microphone speech enhancement algorithm is proposed to improve speech intelligibility for cochlear implant recipients. The proposed algorithm combines harmonic structure estimation with a subsequent ... -
A Study of Voice Production Characteristics of Astronaut Speech During Apollo 11 for Speaker Modeling in Space
(Acoustical Society of America, 2017-03-08)Human physiology has evolved to accommodate environmental conditions, including temperature, pressure, and air chemistry unique to Earth. However, the environment in space varies significantly compared to that on Earth ... -
The Lombard Effect Observed in Speech Produced by Cochlear Implant Users in Noisy Environments: A Naturalistic Study
The Lombard effect is an involuntary response speakers experience in the presence of noise during voice communication. This phenomenon is known to cause changes in speech production such as an increase in intensity, pitch ... -
Analysis of Human Scream and Its Impact on Text-Independent Speaker Verification
(Acoustical Society of America, 2018-08-20)Scream is defined as sustained, high-energy vocalizations that lack phonological structure. Lack of phonological structure is how scream is identified from other forms of loud vocalization, such as "yell." This study ... -
Robust I-Vector Extraction for Neural Network Adaptation in Noisy Environment
In this study, we explore an i-vector based adaptation of deep neural network (DNN) in noisy environment. We first demonstrate the importance of encapsulating environment and channel variability into i-vectors for DNN ... -
I-Vector Based Physical Task Stress Detection with Different Fusion Strategies
It is common for subjects to produce speech while performing a physical task where speech technology may be used. Variabilities are introduced to speech since physical task can influence human speech production. These ... -
An Unsupervised Visual-Only Voice Activity Detection Approach Using Temporal Orofacial Features
Detecting the presence or absence of speech is an important step toward building robust speech-based interfaces. While previous studies have made progress on voice activity detection (VAD), the performance of these systems ... -
Frequency Offset Correction in Single Sideband (SSB) Speech by Deep Neural Network for Speaker Verification
Communication system mismatch represents a major influence for loss in speaker recognition performance. This paper considers a type of nonlinear communication system mismatch- modulation/ demodulation (Mod/DeMod) carrier ... -
Evaluation and Calibration of Short-Term Aging Effects in Speaker Verification
A speaker verification evaluation is presented on the Multisession Audio Research Project (MARP) corpus, for which speakers were recorded at regular intervals, in consistent conditions, over a period of three years. It is ... -
A New Front-End for Classification of Non-Speech Sounds: A Study on Human Whistle
Speech/non-speech sound classification is an important problem in audio diarization, audio document retrieval and advanced human interfaces. The focus of this study is on the development of spectral and temporal acoustic ... -
Anti-Spoofing System: An Investigation of Measures to Detect Synthetic and Human Speech
Automatic Speaker Verification (ASV) systems are prone to spoofing attacks of various kinds. In this study, we explore the effects of different features and spoofing algorithms on a state-of-the-art i-vector speaker ... -
Laughter and Filler Detection in Naturalistic Audio
Laughter and fillers are common phenomenon in speech, and play an important role in communication. In this study, we present Deep Neural Network (DNN) and Convolutional Neural Network (CNN) based systems to classify ... -
Physical Task Stress and Speaker Variability in Voice Quality
The presence of physical task stress induces changes in the speech production system which in turn produces changes in speaking behavior. This results in measurable acoustic correlates including changes to formant center ... -
Compensation of SNR and Noise Type Mismatch using an Environmental Sniffing Based Speech Recognition Solution
Multiple-model based speech recognition (MMSR) has been shown to be quite successful in noisy speech recognition. Since it employs multiple hidden Markov model (HMM) sets that correspond to various noise types and ...
RSS 1.0