Papers with Code 아카이브

Speech Emotion Recognition

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하위 태스크: 4
평가표: 17
보존된 평가 기록: 48

Speech Emotion Recognition is a task of speech processing and computational paralinguistics that aims to recognize and categorize the emotions expressed in spoken language. The goal is to determine the emotional state of a speaker, such as happiness, anger, sadness, or frustration, from their speech patterns, such as prosody, pitch, and rhythm.

For multimodal emotion recognition, please upload your result to Multimodal Emotion Recognition on IEMOCAP

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/paulskeie/stadnamn

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Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks

https://github.com/pengming617/bert_classification

Attention Is All You Need

https://github.com/pengshuyuan/Bert

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https://github.com/penny9287/sockeye

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https://github.com/pennylaneai/generative-quantum-states

Attention Is All You Need

https://github.com/petsol/ContinuousControl_UnityAgent_DDPG_Udacity

Continuous control with deep reinforcement learning

https://github.com/phohenecker/pytorch-transformer

Attention Is All You Need

https://github.com/pikabite/segmentations_tf2

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/pingheng001/Cnn-Bert

Attention Is All You Need

https://github.com/piquil/rydberggpt

Attention Is All You Need

https://github.com/piyushpandey615/GAN-based-Image-Compressor-System

High-Fidelity Generative Image Compression

https://github.com/plaidml/tpp-mlir

High-Performance Deep Learning via a Single Building Block

https://github.com/plkmo/Transformer-Eng2French

Attention Is All You Need

https://github.com/pluflou/MLreviewNotebooks

A high-bias, low-variance introduction to Machine Learning for physicists

https://github.com/pomonam/AttentionCluster

Attention Is All You Need

https://github.com/pooruss/ICNet-Paddle2.2.0rc

ICNet for Real-Time Semantic Segmentation on High-Resolution Images

https://github.com/ppocma/ppocma

High-Dimensional Continuous Control Using Generalized Advantage Estimation

https://github.com/prabhakar267/LASER-improved

CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB

https://github.com/prabhupad26/100daysofML

Attention Is All You Need

https://github.com/prajwalgatti/DRL-Collaboration-and-Competition

Continuous control with deep reinforcement learning

https://github.com/prajwalgatti/DRL-Continuous-Control

Continuous control with deep reinforcement learning

https://github.com/prakashSidd18/blind_augmentation

Restormer: Efficient Transformer for High-Resolution Image Restoration

https://github.com/pranshupant/SR-DNS_Net

Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS Data

https://github.com/pridiltal/stray_manuscript

Anomaly Detection in High Dimensional Data

https://github.com/princefr/Light-Head.pytorch

Light-Head R-CNN: In Defense of Two-Stage Object Detector

https://github.com/pritam1322/Machine-Translation-with-Transformer

Attention Is All You Need

https://github.com/psh01087/simple-nfnet

High-Performance Large-Scale Image Recognition Without Normalization

https://github.com/pwc-1/Paper-8/tree/main/audio_spectrogram_transformer

AST: Audio Spectrogram Transformer

https://github.com/pwc-1/Paper-9/tree/main/4/Translation-Invariant/model

Attention Is All You Need

https://github.com/pwc-1/Paper-9/tree/main/4/Two-Layer-ReLU-Network-Analytically

Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent

https://github.com/pwc-1/Paper-9/tree/main/7/TSA_mindspore

TSAM: A Two-Stream Attention Model for Causal Emotion Entailment

https://github.com/pxaris/ccml

AST: Audio Spectrogram Transformer

https://github.com/qccData/qccCorpus

https://github.com/qingsenyangit/AHDRNet

Attention-guided Network for Ghost-free High Dynamic Range Imaging

https://github.com/quereste/implicit-deepfake

High-Resolution Image Synthesis with Latent Diffusion Models

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.