Datasets:
task_categories:
- audio-classification
- automatic-speech-recognition
language:
- en
tags:
- speech
- conversational
- natural
size_categories:
- n<1K
configs:
- config_name: conversations
data_dir: sample_data/conversations
Emo-com
Emo-com is a speech dataset of real emotional conversations between people actively supporting each other. Unlike most emotion datasets, which rely on acted, pseudo-acted, or scripted speech, Emo-com captures genuine emotional expression in the context of mutual support. It reflects the way people actually talk to a close friend when sharing hardships, daily experiences, and jokes.
Conversations range from grief and feelings of being trapped or exhausted to laughing about silly things. We collect data "in the wild," minimizing the unnatural awkwardness that often results when contributors are set up to speak with each other in job-like settings. The people in conversation are navigating real moments of emotional connection, offering comfort and sharing feelings and experiences. The speaker pool includes women and men ages 18 to 84.
Current voice models lack emotional intelligence, as shown by Good-Pal-Bench. Emo-com aims to teach models to be emotionally supportive and to converse the way a close friend would, responding with appropriate tone, timing, and content. The dataset includes a lot of backchannels, interruptions, and active turn-taking, making it particularly well-suited for training full-duplex S2S models.
This is an ongoing data collection effort, and the samples here represent an early release as the dataset continues to grow. The data may be used for commercial purposes.
Recording method
Every speaker records on a Yeti Nano studio microphone with a pop filter (although this can be changed upon request). The source audio is natively recorded with separate speaker channels, preserving each voice in isolation. The samples presented here are combined conversations, mixed from those individual channels into a single stream.
Speaker metadata
Each speaker has the following metadata collected alongside their recordings.
- Gender
- Age range
- City where they grew up
- Race
- Ethnicity
Audio quality and technical analysis
All recordings are studio quality. Averages across the samples in this release.
- Sample rate. 48,000 Hz
- Bit depth. 24-bit
- File format. WAV (converted to MP3 for presentation, but will be provided as WAV)
- Mean SNR. 16.84 dB
- Median RMS. 0.067424
- Average speech ratio. 0.7452
- Spectral centroid. 3.441 kHz
- Frequency content. 19.65 kHz