Papers with Code 아카이브

Speech Emotion Recognition

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

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/annehartebrodt/fc-federated-svd

공식 코드

Federated singular value decomposition for high dimensional data

https://github.com/annehartebrodt/federated-svd

공식 코드

Federated singular value decomposition for high dimensional data

https://github.com/anoncsc/csc

공식 코드

Causal Strategic Classification: A Tale of Two Shifts

https://github.com/anton-kashkin/hifi_vc

공식 코드

HiFi-VC: High Quality ASR-Based Voice Conversion

https://github.com/antoninschrab/fl-mmdagg

공식 코드

AutoML Two-Sample Test

https://github.com/antoninschrab/mmdagg

공식 코드

MMD Aggregated Two-Sample Test

https://github.com/antran89/two-stream-fcan

공식 코드

Two-stream Flow-guided Convolutional Attention Networks for Action Recognition

https://github.com/anugunjnaman/fixed-maml

공식 코드

Fixed-MAML for Few Shot Classification in Multilingual Speech Emotion Recognition

https://github.com/appen/uhv-ots-speech

공식 코드

Scalable Data Annotation Pipeline for High-Quality Large Speech Datasets Development

https://github.com/apple/ml-cvnets

공식 코드

CVNets: High Performance Library for Computer Vision

https://github.com/aquemy/DPSO_experiments

공식 코드

Two-stage Optimization for Machine Learning Workflow

https://github.com/arazd/miread

공식 코드

MIReAD: Simple Method for Learning High-quality Representations from Scientific Documents

https://github.com/arnav10goel/camulenet

공식 코드

Exploring Multilingual Unseen Speaker Emotion Recognition: Leveraging Co-Attention Cues in Multitask Learning

https://github.com/ars22/meta-learning-eval-id-vs-ood

공식 코드

Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution

https://github.com/arsena-k/Word2Vec-bias-extraction

공식 코드

Machine learning as a model for cultural learning: Teaching an algorithm what it means to be fat

https://github.com/arvijj/hfpl

공식 코드

High-fidelity Pseudo-labels for Boosting Weakly-Supervised Segmentation

https://github.com/aryaaftab/light-sernet

공식 코드

Light-SERNet: A lightweight fully convolutional neural network for speech emotion recognition

https://github.com/aryandeshwal/bodi

공식 코드

Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings

https://github.com/asimokby/formality-bias-analysis

공식 코드

In What Languages are Generative Language Models the Most Formal? Analyzing Formality Distribution across Languages

https://github.com/asindel/artfacepoints

공식 코드

ArtFacePoints: High-resolution Facial Landmark Detection in Paintings and Prints

https://github.com/aslp-lab/osum

공식 코드

OSUM: Advancing Open Speech Understanding Models with Limited Resources in Academia

https://github.com/asolitaryman/hflea

공식 코드

Frame-level emotional state alignment method for speech emotion recognition

https://github.com/asteroid-team/asteroid/tree/master/egs/wsj0-mix-var/Multi-Decoder-DPRNN

공식 코드

Multi-Decoder DPRNN: High Accuracy Source Counting and Separation

https://github.com/atechnicolorskye/rrHDI

공식 코드

Robust Inference for High-Dimensional Linear Models via Residual Randomization

https://github.com/athena-team/athena

공식 코드

Speech SIMCLR: Combining Contrastive and Reconstruction Objective for Self-supervised Speech Representation Learning

https://github.com/athena-xiourouppa/NLP-The-Simpsons-and-9-11

공식 코드

Investigating the Impact of 9/11 on The Simpsons through Natural Language Processing

https://github.com/atzheng/DantzigLP

공식 코드

Computing Estimators of Dantzig Selector type via Column and Constraint Generation

https://github.com/audeering/ser-uncertainty-quantification

공식 코드

Are you sure? Analysing Uncertainty Quantification Approaches for Real-world Speech Emotion Recognition

https://github.com/audeering/w2v2-how-to

공식 코드

Dawn of the transformer era in speech emotion recognition: closing the valence gap

https://github.com/auroua/hrstnet

공식 코드

High-Resolution Swin Transformer for Automatic Medical Image Segmentation

https://github.com/autonomousfieldroboticslab/gopro_ros

공식 코드

High Definition, Inexpensive, Underwater Mapping

https://github.com/b04901014/FT-w2v2-ser

공식 코드

Exploring Wav2vec 2.0 fine-tuning for improved speech emotion recognition

https://github.com/bagustris/A-VB2022_CCC

공식 코드

Predicting Affective Vocal Bursts with Finetuned wav2vec 2.0

https://github.com/bagustris/ccc_mse_ser

공식 코드

Evaluation of Error and Correlation-Based Loss Functions For Multitask Learning Dimensional Speech Emotion Recognition

https://github.com/bagustris/deep_mlp_ser

공식 코드

Deep Multilayer Perceptrons for Dimensional Speech Emotion Recognition

https://github.com/bagustris/ravdess_song_speech

공식 코드

On The Differences Between Song and Speech Emotion Recognition: Effect of Feature Sets, Feature Types, and Classifiers

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