UrduSER: A comprehensive dataset for speech emotion recognition in Urdu language
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View abstract on PubMed
Summary
This summary is machine-generated.A new Urdu Speech Emotion Recognition Dataset (UrduSER) was created to address the lack of resources for analyzing emotions in Urdu speech. This dataset features diverse, real-world dialogues from professional actors, enhancing machine learning models for Urdu SER.
Area Of Science
- Speech Emotion Recognition (SER)
- Computational Linguistics
- Machine Learning
Background
- Speech Emotion Recognition (SER) is vital for understanding human-computer interaction and has significant socio-cultural and business applications.
- Existing datasets for Urdu, the 10th most spoken language, are limited in scope, emotion range, and dialogue diversity, hindering real-world SER applications.
- A significant research gap exists in Urdu SER due to the scarcity of comprehensive and diverse speech emotion datasets.
Purpose Of The Study
- To develop a comprehensive and balanced Speech Emotion Recognition Dataset for the Urdu language (UrduSER).
- To address the limitations of existing Urdu SER datasets by incorporating diverse, real-world dialogues and a wider range of emotions.
- To facilitate advancements in machine learning and deep learning models for Urdu speech emotion analysis.
Main Methods
- Collected 3500 speech signals from 10 professional Pakistani actors (balanced gender and age) from YouTube drama serials and telefilms.
- Included seven distinct emotional states: Angry, Fear, Boredom, Disgust, Happy, Neutral, and Sad, with 500 samples per emotion.
- Ensured dialogue diversity with unique content per utterance and provided detailed metadata, including scripts, for each audio sample.
Main Results
- Developed the UrduSER dataset, a comprehensive resource featuring 3500 diverse speech signals.
- The dataset includes a balanced distribution of 500 samples per emotion and 50 samples per actor per emotion.
- Expert validation confirmed the dataset's validity, reliability, and suitability for research and development.
Conclusions
- The UrduSER dataset effectively fills the critical research gap for Urdu speech emotion recognition.
- Its diverse, real-world nature and comprehensive metadata enhance its utility for training robust SER models.
- This resource is expected to significantly advance research and development in Urdu SER, enabling more accurate and nuanced emotion detection in spoken language.
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