# Speech Emotion Recognition

A speech emotion recognition model based on Whisper-Large V3, optimized for the MSP-Podcast dataset, supporting 9 emotion classifications

This is a fine-tuned model based on Audio Spectrogram Transformer (AST), specifically designed for emotion classification in speech audio.

A speech emotion recognition model based on WavLM-Large, supporting English speech emotion classification.

This project utilizes the Whisper model for speech emotion recognition, capable of classifying audio into different emotional categories such as happiness, sadness, and surprise.

This model uses a fine-tuned wav2vec2 (base) architecture, trained on the IEMOCAP dataset for speech emotion recognition tasks.

A baseline model for speech emotion recognition based on the WavLM architecture, designed to predict 8 basic emotion categories

A speech emotion recognition model based on wav2vec2-large-xlsr-53, supporting 7 emotion classifications

A speech emotion recognition model fine-tuned on the RAVDESS dataset based on the wav2vec2-large-xlsr-53-english model

A fine-tuned audio emotion classification model based on facebook/wav2vec2-base for analyzing emotional states in speech

A fine-tuned audio emotion classification model based on facebook/wav2vec2-base, achieving 73.98% accuracy on the evaluation set

English speech emotion recognition model based on wav2vec2 architecture, fine-tuned on the RAVDESS dataset

Fine-tuned using the WavLM Large model for speech emotion recognition and speaker diarization analysis, supporting multiple emotion classifications

A speech emotion recognition model fine-tuned on the RAVDESS dataset based on DistilHuBERT architecture, achieving 92.36% accuracy

This is a speech emotion recognition model based on the facebook/wav2vec2-base model fine-tuned on the IEMOCAP dataset, achieving 73.9% accuracy on the evaluation set.

A speech emotion recognition model fine-tuned based on facebook/wav2vec2-base, achieving 73.22% accuracy on the evaluation set

An audio emotion classification model fine-tuned based on wav2vec2-base, used to identify the speaker's emotional state.

This model is a fine-tuned version of the AST model on the shEMO dataset, primarily used for speech emotion recognition tasks.

This model is a fine-tuned version of minoosh/wav2vec2-base-finetuned-ie on the shEMO dataset, primarily used for speech emotion recognition tasks.

A speech emotion recognition model fine-tuned based on facebook/wav2vec2-base, achieving an accuracy of 75.73% on the evaluation set

A speech emotion recognition model fine-tuned based on facebook/wav2vec2-base, achieving an accuracy of 83.67% on the evaluation set

A fine-tuned speech emotion recognition model based on facebook/wav2vec2-base, achieving 84.71% accuracy on the evaluation set

A Spanish audio sentiment classification model fine-tuned on the MESD dataset based on facebook/wav2vec2-base

This is a speech emotion recognition model based on the Wav2Vec2 architecture, adapted from the S3PRL project, designed to identify emotional categories in speech.

This is an emotion recognition model based on the Wav2Vec2-Large model, specifically designed to identify emotion categories from speech.

A speech emotion recognition model based on the XLSR-Wav2Vec architecture, capable of identifying five basic emotions: anger, disgust, fear, happiness, and sadness.

This model is an emotion recognition model based on the Hubert-Base architecture, trained on the SUPERB emotion recognition task for speech emotion classification

A speech emotion recognition model fine-tuned on Wav2Vec 2.0, capable of identifying 8 English emotions with an accuracy of 82.23% on the RAVDESS dataset

An emotion recognition model based on Hubert-Large pre-trained model for predicting emotion categories in speech

A speech emotion recognition model based on the HuBERT architecture, capable of identifying the emotional state of a speaker from audio.