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Datasets:
UniDataPro
/
speech-emotion-recognition

Tasks:
Automatic Speech Recognition
Audio Classification
Modalities:
Audio
Formats:
soundfolder
Size:
< 1K
Tags:
audio
speech
recognition
emotion
NLP
Libraries:
Datasets
License:
Dataset card Data Studio Files
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README.md ADDED
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+ ---
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+ license: cc-by-nc-nd-4.0
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+ task_categories:
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+ - automatic-speech-recognition
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+ - audio-classification
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+ tags:
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+ - audio
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+ - speech
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+ - recognition
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+ - emotion
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+ - NLP
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+ size_categories:
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+ - 10K<n<100K
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+ ---
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+ # Speech Emotion Recognition
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+ Dataset comprises **30,000+** audio recordings featuring **4** distinct emotions: euphoria, joy, sadness, and surprise. This extensive collection is designed for research in **emotion recognition**, focusing on the nuances of **emotional speech** and the subtleties of **speech signals** as individuals vocally express their feelings.
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+
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+ By utilizing this dataset, researchers and developers can enhance their understanding of **sentiment analysis** and improve **automatic speech processing** techniques. - **[Get the data](https://unidata.pro/datasets/speech-emotion-recognition/?utm_source=huggingface&utm_medium=referral&utm_campaign=speech-emotion-recognition)**
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+
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+ Each audio clip reflects the tone, intonation, and emotional expressions of diverse speakers, including various ages, genders, and cultural backgrounds, providing a comprehensive representation of human emotions. The dataset is particularly valuable for developing and testing recognition systems and classification models aimed at detecting emotions in spoken language.
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+
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+ # 💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at [https://unidata.pro](https://unidata.pro/datasets/speech-emotion-recognition/?utm_source=huggingface&utm_medium=referral&utm_campaign=speech-emotion-recognition) to discuss your requirements and pricing options.
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+
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+ Researchers can leverage this dataset to explore deep learning techniques and develop classification methods that improve the accuracy of emotion detection in real-world applications. The dataset serves as a robust foundation for advancing affective computing and enhancing speech synthesis technologies.
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+
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+ # Frequently Asked Questions
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+
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+ ## Which audio formats are available in the dataset?
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+
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+ The voice dataset contains recordings in WAV, MPEG, and AMR formats. This format diversity is relevant during preprocessing because different codecs and container formats can produce differences in sampling characteristics and compression artifacts.
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+
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+ ## How was the speech dataset collected?
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+
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+ The audio recordings were collected through crowdsourcing platforms. This means the dataset was assembled from contributions by multiple participants rather than from a single controlled recording session. Such collection can introduce useful variation in voices, speaking styles, recording equipment, and individual acoustic characteristics.
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+
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+ ## How can the metadata support bias analysis in an emotion detection dataset?
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+
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+ The metadata provides text content together with speaker-related attributes such as gender, age, and country. You can use these fields to analyze whether a model’s predictions are associated with demographic or linguistic characteristics.
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+
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+ # Frequently Asked Questions
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+
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+ ## Which audio formats are available in the dataset?
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+
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+ The voice dataset contains recordings in WAV, MPEG, and AMR formats. This format diversity is relevant during preprocessing because different codecs and container formats can produce differences in sampling characteristics and compression artifacts.
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+
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+ ## How was the speech dataset collected?
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+
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+ The audio recordings were collected through crowdsourcing platforms. This means the dataset was assembled from contributions by multiple participants rather than from a single controlled recording session. Such collection can introduce useful variation in voices, speaking styles, recording equipment, and individual acoustic characteristics.
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+
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+ ## How can the metadata support bias analysis in an emotion detection dataset?
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+
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+ The metadata provides text content together with speaker-related attributes such as gender, age, and country. You can use these fields to analyze whether a model’s predictions are associated with demographic or linguistic characteristics.
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+
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+ # 🌐 [UniData](https://unidata.pro/datasets/speech-emotion-recognition/?utm_source=huggingface&utm_medium=referral&utm_campaign=speech-emotion-recognition) provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects
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