speech-emotion-recognition

Vocal Bursts Type Prediction

3260 papers • 126 benchmarks • 313 datasets

predict the type of given vocal bursts

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in speech-emotion-recognition

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HUME-VB

Libraries

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Use these libraries to find speech-emotion-recognition models and implementations

modelscope/adaseq

2 papers 335

Datasets

HUME-VB

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No subtasks available.

Most implemented papers

Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding

Jiawei Han, Heng Ji, Xiang Ren, Wenqi He, Meng Qu, Clare R. Voss•Tue Feb 16 2016

A global objective is formulated for learning the embeddings from text corpora and knowledge bases, which adopts a novel margin-based loss that is robust to noisy labels and faithfully models type correlation derived from knowledge bases.

148

Content

shanzhenren/AFET

2 papers 58

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SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification

S. Harrer, Jianbin Tang, Umar Asif, Subhrajit Roy•Thu Mar 07 2019

SeizureNet, a deep learning framework which learns multi-spectral feature embeddings using an ensemble architecture for cross-patient seizure type classification, is presented and shown to improve the accuracy of smaller networks through knowledge distillation for applications with low-memory constraints.

88 0

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Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss

Denilson Barbosa, Peng Xu•Thu Mar 08 2018

This work proposes an end-to-end solution with a neural network model that uses a variant of cross-entropy loss function to handle out-of-context labels, and hierarchical loss normalization to cope with overly-specific ones in FETC.

93 0

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Vehicle and license plate recognition with novel dataset for toll collection

Saeed Anwar, Muhammad Usama, Hafeez Anwar, Muhammad Shahid, A. Anwar, Helmuth Hlavacs•Thu Feb 10 2022

An automatic toll tax collection framework designed for challenging conditions, consisting of three sequential steps: vehicle type recognition, license plate localization, and license plate reading, using state-of-the-art YOLO models.

21 0

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Typilus: neural type hints

Miltiadis Allamanis, Earl T. Barr, Soline Ducousso, Zheng Gao•Sun Apr 05 2020

A graph neural network model is presented that predicts types by probabilistically reasoning over a program’s structure, names, and patterns and can employ one-shot learning to predict an open vocabulary of types, including rare and user-defined ones.

146 0

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Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning

M. Zitnik, C. Langlotz, Jiawei Han, Yuhao Zhang, Jingbo Shang, Xiang Ren, Yu Zhang, Xuan Wang•Mon Jan 29 2018

A multi-task learning framework for BioNER to collectively use the training data of different types of entities and improve the performance on each of them, which achieves substantially better performance than state-of-the-art BioNER systems and baseline neural sequence labeling models.

260 0

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Car type recognition with Deep Neural Networks

H. Huttunen, Ke Chen, Fatemeh Shokrollahi Yancheshmeh•Mon Feb 22 2016

Two data driven frameworks are considered: a deep neural network and a support vector machine using SIFT features for automatic recognition of cars of four types: Bus, Truck, Van and Small car.

88 0

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Context-Dependent Fine-Grained Entity Type Tagging

D. Gillick, Kuzman Ganchev, N. Lazic, Jesse Kirchner, David Huynh•Tue Dec 02 2014

This work proposes the task of context-dependent fine type tagging, where the set of acceptable labels for a mention is restricted to only those deducible from the local context (e.g. sentence or document).

146 0

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Ludwig: a type-based declarative deep learning toolbox

Piero Molino, Yaroslav Dudin, Sai Sumanth Miryala•Mon Sep 16 2019

Ludwig is a flexible, extensible and easy to use toolbox which allows users to train deep learning models and use them for obtaining predictions without writing code, and introduces a general modularized deep learning architecture called Encoder-Combiner-Decoder that can be instantiated to perform a vast amount of machine learning tasks.

71 0

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Learning Type-Aware Embeddings for Fashion Compatibility

D. Forsyth, Bryan A. Plummer, Mariya I. Vasileva, Krishna Dusad, Shreya Rajpal, Ranjitha Kumar•Sat Mar 24 2018

This paper presents an approach to learning an image embedding that respects item type, and jointly learns notions of item similarity and compatibility in an end-to-end model.

249 0

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