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John H. L. Hansen
University of Texas at Dallas
709Publications
48H-index
11.6kCitations
Publications 708
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#2Dwight IrvinH-Index: 1
Last.John H. L. HansenH-Index: 48
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Sep 15, 2019 in INTERSPEECH (Conference of the International Speech Communication Association)
#1Kong LeeH-Index: 1
#2Ville Hautamäki (University of Eastern Finland)H-Index: 17
Last.Nicholas Evans (EURECOM: Institut Eurécom)H-Index: 25
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The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE). The latest edition of such joint submission was in SRE 2018, in which the I4U submission was among the best-performing systems. SRE'18 also marks the 10-year anniversary of I4U consortium into NIST SRE series of evaluation. The primary objective of the current paper is to summarize the results and lessons learned based on the twelve subsystems and their fusion submitted to SRE'18. It is ...
1 CitationsSource
Sep 15, 2019 in INTERSPEECH (Conference of the International Speech Communication Association)
#1John H. L. Hansen (UTD: University of Texas at Dallas)H-Index: 48
Last.Abhijeet Sangwan (UTD: University of Texas at Dallas)H-Index: 10
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Sep 15, 2019 in INTERSPEECH (Conference of the International Speech Communication Association)
#2Hussnain Ali (UTD: University of Texas at Dallas)H-Index: 5
Last.John H. L. Hansen (UTD: University of Texas at Dallas)H-Index: 48
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Sep 15, 2019 in INTERSPEECH (Conference of the International Speech Communication Association)
#1Qing Wang (SYSU: Sun Yat-sen University)H-Index: 38
#2Pengcheng Guo (NPU: Northwestern Polytechnical University)
Last.John H. L. Hansen (UTD: University of Texas at Dallas)H-Index: 48
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Sep 15, 2019 in INTERSPEECH (Conference of the International Speech Communication Association)
#2Abhijeet SangwanH-Index: 4
Last.John H. L. Hansen (UTD: University of Texas at Dallas)H-Index: 48
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#1John H. L. Hansen (UTD: University of Texas at Dallas)H-Index: 48
#2Maryam Najafian (UTD: University of Texas at Dallas)H-Index: 7
Last.Beth Rous (UK: University of Kentucky)H-Index: 13
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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 which activity areas stimulate verbal communication can help promote more efficient language development. In this study, we present a two-stage children vocal engagement prediction system that consists of (1) a near to real-time, noise robust system that measures the duration o...
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#1Midia YousefiH-Index: 2
#2Soheil KhorramH-Index: 6
Last.John H. L. HansenH-Index: 48
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Single-microphone, speaker-independent speech separation is normally performed through two steps: (i) separating the specific speech sources, and (ii) determining the best output-label assignment to find the separation error. The second step is the main obstacle in training neural networks for speech separation. Recently proposed Permutation Invariant Training (PIT) addresses this problem by determining the output-label assignment which minimizes the separation error. In this study, we show that...
2 Citations
#1Nursadul MamunH-Index: 3
#2Ria GhoshH-Index: 1
Last.John H. L. Hansen (UTD: University of Texas at Dallas)H-Index: 48
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Speaker recognition is a biometric modality that uses underlying speech information to determine the identity of the speaker. Speaker Identification (SID) under noisy conditions is one of the challenging topics in the field of speech processing, specifically when it comes to individuals with cochlear implants (CI). This study analyzes and quantifies the ability of CI-users to perform speaker identification based on direct electric auditory stimuli. CI users employ a limited number of frequency b...
1 Citations
#1Nursadul Mamun (UTD: University of Texas at Dallas)H-Index: 3
#2Soheil Khorram (UTD: University of Texas at Dallas)H-Index: 6
Last.John H. L. Hansen (UTD: University of Texas at Dallas)H-Index: 48
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Attempts to develop speech enhancement algorithms with improved speech intelligibility for cochlear implant (CI) users have met with limited success. To improve speech enhancement methods for CI users, we propose to perform speech enhancement in a cochlear filter-bank feature space, a feature-set specifically designed for CI users based on CI auditory stimuli. We leverage a convolutional neural network (CNN) to extract both stationary and non-stationary components of environmental acoustics and ...
1 Citations
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