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computer-vision-5

Cross-corpus

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Readability-based Sentence Ranking for Evaluating Text Simplification

Sowmya Vajjala, Walt Detmar Meurers•Thu Mar 17 2016

A new method for evaluating the readability of simplified sentences through pair-wise ranking, which correctly identifies the ranking of simplified and unsimplified sentences in terms of their reading level with an accuracy of over 80%, significantly outperforming previous results.

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Generative Adversarial Nets for Multiple Text Corpora

Baiyang Wang, D. Klabjan•Sun Dec 24 2017

This work demonstrates the GAN models on real-world text data sets from different corpora, and shows that embeddings from both models lead to improvements in supervised learning problems.

15 0

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Transfer Learning for Improving Speech Emotion Classification Accuracy

Junaid Qadir, S. Latif, R. Rana, Shahzad Younis, J. Epps•Thu Jan 18 2018

Evaluations on five different corpora in three different languages show that Deep Belief Networks (DBNs) offer better accuracy than previous approaches on cross-corpus emotion recognition, relative to a Sparse Autoencoder and Support Vector Machine (SVM) baseline system.

118 0

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Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages

A. Qayyum, Junaid Qadir, S. Latif, Muhammad Usman•Fri Nov 30 2018

This study investigates the problem of cross-lingual emotion recognition for Urdu language and contributes URDU—the first ever spontaneous Urdu-language speech emotion database and suggests various interesting aspects for designing more adaptive emotion recognition system for such limited languages.

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A Novel Policy for Pre-trained Deep Reinforcement Learning for Speech Emotion Recognition

Björn Schuller, R. Rana, Thejan Rajapakshe, Sara Khalifa, Jiajun Liu•Sun Jan 03 2021

A novel policy, the “Zeta policy” tailored for SER is introduced and pre- training in deep RL is introduced to achieve a faster learning rate and support that pre-training can reduce training time and is robust to a cross-corpus scenario.

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Accounting for Variations in Speech Emotion Recognition with Nonparametric Hierarchical Neural Network

E. Provost, Lance Ying, Amrit Romana•Wed Sep 08 2021

The Nonparametric Hierarchical Neural Network (NHNN), a lightweight hierarchical neural network model based on Bayesian nonparametric clustering, is proposed and it is shown that the NHNN models are able to learn group-specific features and bridge the performance gap between groups.

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A Neural Pairwise Ranking Model for Readability Assessment

Sowmya Vajjala, Justin Lee•Sun Mar 13 2022

This paper proposes the first neural pairwise ranking model for ARA, and shows the first results of cross-lingual, zero-shot evaluation of ARA with neural models.

37 0

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Dawn of the Transformer Era in Speech Emotion Recognition: Closing the Valence Gap

Maximilian Schmitt, Björn Schuller, Johannes Wagner, Andreas Triantafyllopoulos, H. Wierstorf, F. Eyben•Sun Mar 13 2022

Transformer-based architectures are more robust compared to a CNN-based baseline and fair with respect to gender groups, but not towards individual speakers, and it is shown that their success on valence is based on implicit linguistic information, which explains why they perform on-par with recent multimodal approaches that explicitly utilise textual information.

416 0

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Cross-corpus | State-of-the-Art