---
title: "Speech Emotion Recognition Dataset"
description: "30,000+ audio 4 emotions"
url: "https://unidata.pro/datasets/speech-emotion-recognition/"
date_modified: "2025-12-09T11:44:30+03:00"
language: "en-US"
---
The dataset comprises over 30,000 audio recordings labeled with four distinct speech emotions: euphoria, joy, sadness, and surprise. Designed for speech emotion recognition, speech recognition, and sentiment analysis, the dataset includes rich audio features, human-labeled metadata, and diverse emotional expressions for training advanced machine learning models.

## Dataset Structure

### The Numbers Section

**Numbered list:**

- **Number:** 30,000+ — **Text:** audio
- **Number:** 4 — **Text:** emotions

### Tooltips Section

**Tooltip items:**

- **Name:** Emotion Recognition
- **Name:** Speech Analysis
- **Name:** Audio
- **Name:** ASR
- **Name:** NLP
- **Name:** Machine learning

### Dataset Information

**Table with data:**

| 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 |

**Media Slider:**

- **Image in the slider:** ![Example of the data](https://unidata.pro/wp-content/uploads/2024/12/image3-dataset-1.webp)
- **Image in the slider:** ![Example of the data](https://unidata.pro/wp-content/uploads/2024/12/image2-dataset1205.webp)

**Link to the sample:** [Download sample](https://drive.google.com/drive/folders/1EgL5_6WdA22dLMAh6mkBhX-7qKjBU5Ke)

### Technical Specifications

**Table with data:**

| Characteristic | Data |
| --- | --- |
| Audio Format | WAV, MPEG, AMR |
| Recording condition | Low background noise |

**Source and data collection methodology:** Source and collection methodology:  Data was collected via crowdsourcing platforms.

### Dataset Use Cases - Slider

**Industry Cards:**

- **Industry:** Artificial Intelligence & Machine Learning — **Title:** Training Models for Emotion Detection in Speech — **Text:** 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.
- **Industry:** Human-Computer Interaction & Voice Assistants — **Title:** Enhancing Empathy in Voice-Driven Systems — **Text:** 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.
- **Industry:** Customer Experience & Sentiment Analysis — **Title:** Improving Emotion-Aware Analytics in Call Centers — **Text:** 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.
- **Industry:** Academic Research & Multimodal Emotion Studies — **Title:** Benchmarking Models for Audio Emotion Classification — **Text:** 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.

### Fact

**FAQs Heading:** FAQs

**List of Questions:**

- **Question:** What is Speech Emotion Recognition Dataset used for? — **Answer:** 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.
- **Question:** Can I request a sample of the dataset before purchasing or downloading it? — **Answer:** 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.
- **Question:** How was the data collected? — **Answer:** The audio recordings were collected using crowdsourcing platforms. All recordings were performed under low background noise conditions, producing high-quality speech signals.
- **Question:** How are Unidata datasets licensed? — **Answer:** 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.
- **Question:** Do Unidata datasets follow GDPR or other data privacy regulations? — **Answer:** 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.
- **Question:** How are Unidata datasets stored? — **Answer:** 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.
- **Question:** Is this a real-world dataset or synthetic data? — **Answer:** 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.
- **Question:** How can a speech emotion recognition dataset improve AI understanding of human communication? — **Answer:** A speech emotion recognition dataset helps AI models learn how emotions are expressed through voice patterns, speech characteristics, and conversational context. It supports the development of systems that can interpret emotional signals for more natural human-computer interactions.
- **Question:** How does emotional diversity help train more accurate speech AI models? — **Answer:** Training models on different emotional expressions allows AI systems to recognize variations in tone, intensity, and speaking style. This improves model robustness when analyzing natural conversations across different users and communication environments.

[Full list of this site's AI-readable pages](https://unidata.pro/llms.txt)