https://github.com/minimalparts/nonce2vec
공식 코드High-risk learning: acquiring new word vectors from tiny data
Papers with Code 아카이브
Speech Emotion Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/mitmedialab/sherlock-project
공식 코드Sherlock: A Deep Learning Approach to Semantic Data Type Detection
https://github.com/mkirchler/deep-2-sample-test
공식 코드https://github.com/mmakiuchi/multimodal_emotion_recognition
공식 코드Multimodal Emotion Recognition with High-level Speech and Text Features
https://github.com/mmezache/HFFTest
공식 코드https://github.com/mnecipkurt/pami20
공식 코드Real-Time Nonparametric Anomaly Detection in High-Dimensional Settings
https://github.com/modelscope/adaseq
공식 코드https://github.com/modelscope/modelscope
공식 코드VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation
https://github.com/monaen/LightFieldReconstruction
공식 코드High-dimensional Dense Residual Convolutional Neural Network for Light Field Reconstruction
https://github.com/mostly-ai/paper-fidelity-accuracy
공식 코드Holdout-Based Fidelity and Privacy Assessment of Mixed-Type Synthetic Data
https://github.com/mrT23/TResNet
공식 코드https://github.com/mrluin/ESFNet-Pytorch
공식 코드ESFNet: Efficient Network for Building Extraction from High-Resolution Aerial Images
https://github.com/mrrezaeiuoft/deep_direct_discriminative_decoder-d4-
공식 코드Deep Direct Discriminative Decoders for High-dimensional Time-series Data Analysis
https://github.com/mrzarei5/vax-culture
공식 코드Vax-Culture: A Dataset for Studying Vaccine Discourse on Twitter
https://github.com/mspranger/icdl2016language
공식 코드Referential Uncertainty and Word Learning in High-dimensional, Continuous Meaning Spaces
https://github.com/mtran14/dp_w2v2
공식 코드A Speech Representation Anonymization Framework via Selective Noise Perturbation
https://github.com/multimedia-berkeley/OriSet
공식 코드OrigamiSet1.0: Two New Datasets for Origami Classification and Difficulty Estimation
https://github.com/mustafaCoskunAgu/ScNP
공식 코드Graph Convolutional Networks Meet with High Dimensionality Reduction
https://github.com/mvanness354/missing_indicator_method
공식 코드https://github.com/mvasil/fashion-compatibility
공식 코드https://github.com/myHaiven/data-collection
공식 코드BERSting at the Screams: A Benchmark for Distanced, Emotional and Shouted Speech Recognition
https://github.com/naamahadad/A-Two-Step-Disentanglement-Method
공식 코드https://github.com/naivete5656/WSISPDR
공식 코드Weakly Supervised Cell Instance Segmentation by Propagating from Detection Response
https://github.com/natkowalczyk/thermal-mask-classification-and-detection
공식 코드https://github.com/natspeech/natspeech
공식 코드PortaSpeech: Portable and High-Quality Generative Text-to-Speech
https://github.com/neclow/serab
공식 코드SERAB: A multi-lingual benchmark for speech emotion recognition
https://github.com/neuralchen/CooGAN
공식 코드CooGAN: A Memory-Efficient Framework for High-Resolution Facial Attribute Editing
https://github.com/neuralchen/SimSwap
공식 코드SimSwap: An Efficient Framework For High Fidelity Face Swapping
https://github.com/neuroailab/Neural-Alignment
공식 코드Two Routes to Scalable Credit Assignment without Weight Symmetry
https://github.com/nicaogr/multiresolution_texture
공식 코드High resolution neural texture synthesis with long range constraints
https://github.com/nicob15/deepkernellearningofdynamicalmodels
공식 코드Deep Kernel Learning of Dynamical Models from High-Dimensional Noisy Data
https://github.com/nifangbaage/coordfill
공식 코드CoordFill: Efficient High-Resolution Image Inpainting via Parameterized Coordinate Querying
https://github.com/nilesc/Long-Structured-Debate-Generation-and-Evaluation
공식 코드https://github.com/ninglab/g2retro
공식 코드$\mathsf{G^2Retro}$ as a Two-Step Graph Generative Models for Retrosynthesis Prediction
https://github.com/nlpAThits/figet-hyperbolic-space
공식 코드https://github.com/nmevenkamp/pcams
공식 코드Variational Multi-Phase Segmentation using High-Dimensional Local Features
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.