Emotions on Audio Dataset

The audio dataset consists of a collection of texts spoken with four distinct emotions. These texts are spoken in English and represent four different emotional states: euphoria, joy, sadness and surprise. Each audio clip captures the tone, intonation, and nuances of speech as individuals convey their emotions through their voice.

The dataset includes a diverse range of speakers, ensuring variability in age, gender, and cultural backgrounds, allowing for a more comprehensive representation of the emotional spectrum.

The dataset is labeled and organized based on the emotion expressed in each audio sample, making it a valuable resource for emotion recognition and analysis. Researchers and developers can utilize this dataset to train and evaluate machine learning models and algorithms, aiming to accurately recognize and classify emotions in speech.

The audio dataset also provides an opportunity for various applications:

sentiment analysis
automatic emotion detection
emotional speech synthesis.
voice assistants
customer service
mental health analysis
entertainment industries

Get the dataset

This is just an example of the data

Leave a request on https://trainingdata.pro/datasets to discuss your requirements, learn about the price and buy the dataset.

Content

files: includes folders corresponding to people and containing text spoken in English in 4 different manners: euphoric, joyfully, sad and surprised
.csv file: contains information about people in the dataset

File with the extension .csv

includes the following information for each set of media files:

set_id: link to the set of audio files,
text: text spoken in the audio set,
gender: gender of the person,
age: age of the person,
country: country of the person

Audio with emotions might be collected in accordance with your requirements.

TrainingData

More datasets in TrainingData's Kaggle account: https://www.kaggle.com/trainingdatapro/datasets

TrainingData's GitHub: https://github.com/Trainingdata-datamarket/TrainingData_All_datasets

keywords: voice assistants speech processing, ser, audio signals, artificial intelligence, ai, emotion recognition, emotional data, recognize human emotions, conversational analytics, speech features, computational paralinguistics, dataset, speech-to-text dataset, speech recognition dataset, audio dataset, automatic speech recognition dataset, voice dataset, human speech recognition, audio recording of human voices, speech recognition russian, stt/asr, deep speech, deep learning, machine learning, human-labeled sound, sound vocabulary, voice dataset, audio data