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Emotion Diarization Wavlm Large

Developed by speechbrain
Fine-tuned using the WavLM Large model for speech emotion recognition and speaker diarization analysis, supporting multiple emotion classifications
Downloads 1,128
Release Time : 7/4/2023

Model Overview

This model, through fine-tuning the WavLM Large architecture, can identify emotional components in speech recordings and determine their temporal boundaries, suitable for emotion analysis and speaker diarization tasks.

Model Features

Training on Multiple Emotion Datasets
The model is trained on five major emotion datasets (Zaion, IEMOCAP, RAVDESS, etc.), providing broad emotion recognition capabilities.
Time Boundary Detection
Not only identifies emotion types but also accurately determines the temporal boundaries of emotional segments.
High-precision Emotion Classification
Achieves a 29.7% Emotion Diarization Error Rate (EDER) on the Zaion test set.

Model Capabilities

Speech Emotion Recognition
Speaker Diarization Analysis
Emotion Time Boundary Detection
Multi-emotion Classification

Use Cases

Emotion Analysis
Customer Service Dialogue Analysis
Analyze customer emotional changes in service dialogues
Identify key emotional nodes such as anger and happiness
Psychological State Assessment
Assess the speaker's psychological state through speech analysis
Detect emotional features such as depression and anxiety
Media Analysis
Film and TV Emotion Analysis
Analyze emotional changes of characters in films and TV shows
Generate emotion timelines to assist content analysis
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