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Speech Emotion Recognition Dataset

Speech Emotion Recognition Dataset comprises over 30,000 audio recordings labeled with four distinct speech emotions: euphoria, joy, sadness, and surprise. It is designed to train emotion recognition and speech recognition systems using rich audio features, human-labeled metadata, and diverse emotional expressions for advanced machine learning and sentiment analysis tasks.

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  • audio
    30,000+
  • emotions
    4
Example of the data
  • Emotion Recognition
  • Speech Analysis
  • Audio
  • ASR
  • NLP
  • Machine learning

Speech Emotion Recognition Dataset comprises over 30,000 audio recordings labeled with four distinct speech emotions: euphoria, joy, sadness, and surprise. It is designed to train emotion recognition and speech recognition systems using rich audio features, human-labeled metadata, and diverse emotional expressions for advanced machine learning and sentiment analysis tasks.

Get in touch Download sample
  • Emotion Recognition
  • Speech Analysis
  • Audio
  • ASR
  • NLP
  • Machine learning
  • audio
    30,000+
  • emotions
    4

Dataset Info

Characteristic Data
Description Dataset of audio recordings featuring 4 distinct emotions
Data types Audio
Tasks Emotion recognition, NLP
Total number of files 30,000+
Emotion Euphoria, joy, sadness, and surprise
Labeling Annotation (text content, gender, age and country)
Gender Male, Female
Example of the data
Example of the data
Download sample

Technical
Characteristics

Characteristic Data
Audio Format WAV, mpeg, amr
Recording condition Low background noise
Source and collection methodology: Data was collected via crowdsourcing platforms.

Dataset Use Cases

  • Artificial Intelligence & Machine Learning

    Training Models for Emotion Detection in Speech

    Speech Emotion Recognition Dataset provides high-quality audio recordings and speech signals labeled with distinct emotion classes. It serves as essential training data for machine learning and deep learning models that perform classification tasks in emotion recognition. The dataset consists of balanced samples for detecting positive and negative emotions in natural speech corpus data.

  • Human-Computer Interaction & Voice Assistants

    Enhancing Empathy in Voice-Driven Systems

    This dataset helps developers build recognition systems that understand human emotions from speech signals. By analyzing audio features such as tone, pitch, and rhythm, voice assistants and conversational agents can respond with greater sensitivity to emotional expressions. The dataset enables more natural and context-aware speech recognition applications.

  • Customer Experience & Sentiment Analysis

    Improving Emotion-Aware Analytics in Call Centers

    Organizations use this emotion detection dataset to develop sentiment analysis tools that assess emotions expressed in customer calls. It contains labeled audio files representing diverse emotional tones, supporting classification methods that recognize frustration, satisfaction, or neutrality. Such models enhance quality monitoring and customer satisfaction analysis in speech-based communication systems.

  • Academic Research & Multimodal Emotion Studies

    Benchmarking Models for Audio Emotion Classification

    Researchers utilize this Speech Emotion Recognition Dataset to study multimodal emotion detection and speech emotions across languages and demographics. The corpus contains annotated audio samples with defined acoustic features, making it ideal for evaluating pre-trained models and emotion recognition algorithms. It supports comparative analysis between audio data types, fostering advancements in speech-based emotion recognition research.

FAQs

What is Speech Emotion Recognition Dataset used for?
This dataset is primarily used for emotion recognition, sentiment analysis, and speech-based AI research. It helps in building and fine-tuning emotion detection models for applications such as virtual assistants, customer interaction systems, and human-computer interaction technologies.
What is included in this dataset?
The dataset contains over 30,000 audio recordings of human speech expressing four distinct emotions - euphoria, joy, sadness, and surprise. Each sample includes detailed metadata annotations such as text content, gender, age, and country of the speaker to support multimodal emotion classification tasks.
Can I request a sample of the dataset before purchasing or downloading it?
Yes. Unidata provides free sample data for evaluation and testing. The sample includes a subset of audio recordings with labeled emotions, helping you assess the quality, file formats, and annotation structure before purchasing the complete dataset.
How was the data collected?
The audio recordings were collected using crowdsourcing platforms. All recordings were performed under low background noise conditions, producing high-quality speech signals.
How are Unidata datasets licensed?
Unidata datasets follow a dual-licensing model: free dataset samples are offered for testing and validation, while full datasets are available for purchase. This ensures users can evaluate audio quality and labeling accuracy before acquiring the full speech dataset.
Do Unidata datasets follow GDPR or other data privacy regulations?
Yes. All Unidata datasets are curated in accordance with GDPR and relevant international privacy standards. Data collection is conducted through ethically approved sources, ensuring anonymized and lawful handling of speaker information across all regions.
How are Unidata datasets stored?
All datasets are securely stored on AWS cloud infrastructure, which ensures scalability, reliability, and compliance with ISO 27001 and ISO 27701 standards. This guarantees a privacy-focused and high-availability environment for managing sensitive audio data and speech recordings.
Is this a real-world dataset or synthetic data?
This is a real-world speech dataset, containing genuine audio recordings of human speakers expressing natural emotions. No synthetic or AI-generated voices are included, ensuring that all audio samples reflect authentic emotional speech patterns for realistic model training.
Still have questions about using Unidata datasets? Read our user-guides

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