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Speechbrain Emotion Recognition Openvino

Developed by psakamoori
This model uses a fine-tuned wav2vec2 (base) architecture, trained on the IEMOCAP dataset for speech emotion recognition tasks.
Downloads 13
Release Time : 6/11/2024

Model Overview

This is a wav2vec2-based speech emotion recognition model that combines convolutional and residual blocks, extracts embedding features through attention statistics pooling, and is trained using additive margin Softmax loss.

Model Features

Efficient Emotion Recognition
Utilizes the pre-trained wav2vec2 model for fine-tuning to achieve efficient and accurate emotion recognition
Attention Statistics Pooling
Adopts attention statistics pooling to extract more effective speech emotion features
OpenVINO Support
Supports accelerated inference via OpenVINO and can run on various Intel hardware

Model Capabilities

Speech Emotion Classification
Real-time Emotion Recognition
Multi-emotion State Detection

Use Cases

Human-Computer Interaction
Smart Customer Service Emotion Analysis
Analyzes emotional states in customer voices to improve service quality
Can recognize basic emotional states such as anger and happiness
Mental Health
Emotional State Monitoring
Analyzes user emotional changes through speech
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