3,000 semi-natural Persian speech utterances totaling 3 hours and 25 minutes of audio extracted from online radio plays. The collection features 87 native speakers expressing five primary emotional states including anger, fear, happiness, and sadness.
Use Cases
- Train speech emotion recognition (SER) models to classify Persian audio into categories like anger, fear, or happiness
- Evaluate speaker-independent emotion detection performance across the 87 distinct speaker identities
- Study the prosodic features of the Persian language using the semi-natural speech samples from radio plays
Strengths
- 3,000 semi-natural utterances totaling 205 minutes of audio data
- Speech samples from 87 distinct native-Persian speakers
- Categorized into five basic emotion classes including anger, fear, happiness, and sadness
- Data sourced from online radio plays to capture realistic emotional expression