SON

SON is a speech dataset of natural, unscripted conversations between friends. Each recording captures two speakers answering casual Q&A-style prompts together, producing spontaneous dialogue rich in interruptions, backchanneling, and paralinguistic features such as laughter, hesitation, and filler words.

About the data

All conversations are between friends discussing casual topics. The content is driven by fun, lighthearted prompts that encourage natural interaction. Because speakers know each other and are responding spontaneously, the recordings contain frequent overlapping speech, backchanneling (e.g. "mhm", "yeah"), and expressive vocalizations that are typical of real-world conversation but underrepresented in most speech datasets.

Each recording includes human-verified transcripts with speaker diarization, timestamps, and audio tags for non-speech events (e.g. [laughing], [sighing]). Transcripts are full verbatim, preserving every utterance including filler words, false starts, and overlapping speech. Transcript samples are available in the dataset viewer.

The audio is dual channel, with each speaker natively recorded on a separate channel. This makes SON suitable for tasks that require speaker-separated input, such as diarization evaluation or per-speaker analysis.

Strengths

The primary strength of this dataset is its naturalness. Because these are real conversations between friends, the audio captures genuine full-duplex speech elements that are difficult to simulate or elicit in scripted settings. These include backchanneling, interruptions, overlapping speech, laughter, and other paralinguistic cues that reflect how people actually talk to each other.

Technical specs

Metric	Value
File format	WAV (MP3 in this dataset card)
Sample rate	48,000 Hz
Bit depth	16-bit
Segment duration	120 seconds each
Average SNR	21.74 dB
Average speech ratio	68.6%
Average spectral centroid	2.539 kHz
Average frequency content	11.581 kHz

Speaker metadata

Each recording includes demographic metadata for both speakers when available.

• Accent (inferred from IP address)
• Gender