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20162020
most citedMultiple instance learning with graph neural networks

55 citations · 126 across the 6 of their papers we have counts for

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5 papers · 1 filter

eess.AS20193 cited

I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences

Kong Aik Lee, Ville Hautamaki, Tomi Kinnunen +43

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…

eess.AS201917 cited

Towards adversarial learning of speaker-invariant representation for speech emotion recognition

Ming Tu, Yun Tang, Jing Huang +2

Speech emotion recognition (SER) has attracted great attention in recent years due to the high demand for emotionally intelligent speech interfaces. Deriving speaker-invariant repr…

eess.AS2018

Investigating the role of L1 in automatic pronunciation evaluation of L2 speech

Ming Tu, Anna Grabek, Julie Liss +1

Automatic pronunciation evaluation plays an important role in pronunciation training and second language education. This field draws heavily on concepts from automatic speech recog…

eess.AS2018

Simulating dysarthric speech for training data augmentation in clinical speech applications

Yishan Jiao, Ming Tu, Visar Berisha +1

Training machine learning algorithms for speech applications requires large, labeled training data sets. This is problematic for clinical applications where obtaining such data is…

eess.AS2018

A Discriminative Acoustic-Prosodic Approach for Measuring Local Entrainment

Megan M. Willi, Stephanie A. Borrie, Tyson S. Barrett +2

Acoustic-prosodic entrainment describes the tendency of humans to align or adapt their speech acoustics to each other in conversation. This alignment of spoken behavior has importa…