| --- |
| language: |
| - zh |
| - en |
| - pt |
| - es |
| - fr |
| - ja |
| - ko |
| - yue |
| - de |
| - it |
| pretty_name: "Global Conversational Speech — Healthcare, Meetings, Call Centers" |
| license: other |
| license_name: commercial-license |
| license_link: https://usergy.ai/contact |
| task_categories: |
| - automatic-speech-recognition |
| - audio-classification |
| tags: |
| - audio |
| - speech |
| - conversational |
| - healthcare |
| - medical |
| - multilingual |
| - native-speakers |
| - stereo |
| - channel-separated |
| - speaker-diarization |
| - asr |
| - transcription |
| - meetings |
| - call-center |
| - professional-recording |
| size_categories: |
| - 100K<n<1M |
| annotations_creators: |
| - machine-generated |
| - expert-generated |
| language_creators: |
| - crowdsourced |
| extra_gated_prompt: | |
| ## Commercial Dataset Access |
| |
| This dataset is available for **commercial licensing**. |
| |
| By requesting access, you'll receive: |
| - Full dataset specifications and sample files |
| - Pricing information ($65/hour) |
| - Direct contact with our team |
| |
| We typically respond within 24 hours. |
| extra_gated_fields: |
| Name: text |
| Company: text |
| Email: text |
| "Intended Use Case": text |
| --- |
| |
| # Global Conversational Speech Dataset |
|
|
| **305 hours. 18 locales. Real conversations.** |
|
|
| Not scraped from YouTube. Not recorded by anonymous crowds who don't speak the language. Every conversation in this dataset traces back to verified native speakers we know by name. |
|
|
| --- |
|
|
| ## The [Human] Standard |
|
|
| Most speech datasets are built the same way: scrape the internet, hire anonymous contractors, run it through automated QC, ship it. The result? Models that are confidently wrong. |
|
|
| We built this dataset differently. |
|
|
| - **Native speakers only** — Every recording made by verified speakers in their native language |
| - **Real conversations** — Unscripted, natural dialogue between two people who actually know how to talk to each other |
| - **Channel-separated stereo** — Each speaker on their own channel, ready for diarization training |
| - **Full traceability** — Every file links back to the person who recorded it |
|
|
| This is the [human] standard for AI data. |
|
|
| --- |
|
|
| ## Dataset Overview |
|
|
| | Metric | Value | |
| |--------|-------| |
| | **Total Duration** | 305 hours | |
| | **Total Files** | 928 | |
| | **Total Size** | ~199 GB | |
| | **Locales** | 18 | |
| | **Domains** | Healthcare, Meetings, Call Centers | |
| | **Format** | WAV, PCM, Stereo | |
| | **Sample Rate** | 44.1 kHz / 48 kHz | |
| | **Speakers per File** | 2 (98.6% of files) | |
| | **Transcripts** | 100% coverage, word-level timestamps | |
|
|
| --- |
|
|
| ## Audio Specifications |
|
|
| - **Format:** WAV (PCM 16-bit / 24-bit lossless) |
| - **Sample Rate:** 44.1 kHz or 48 kHz |
| - **Channels:** Stereo (2-channel, speaker-separated) |
| - **Recording Platform:** Zencastr / Riverside (professional remote recording) |
| - **SNR:** 45-51 dB (studio-quality) |
| - **Speech Type:** Natural, unscripted conversational dialogue |
| - **Typical Duration:** 10-60 minutes per recording |
|
|
| All audio files are professionally recorded with channel separation — Speaker A on left channel, Speaker B on right channel. Ready for speaker diarization training out of the box. |
|
|
| --- |
|
|
| ## Supported Languages |
|
|
| | Locale | Language | Hours | Domain Focus | |
| |--------|----------|-------|--------------| |
| | zh-CN | Chinese (Mandarin) | 34h | Healthcare, Meetings | |
| | en-US | English (US) | 31h | Healthcare, Meetings | |
| | pt-PT | Portuguese (Portugal) | 30h | Healthcare | |
| | pt-BR | Portuguese (Brazil) | 30h | Healthcare | |
| | es-MX | Spanish (Mexico) | 28h | Healthcare, Meetings | |
| | en-GB | English (UK) | 26h | Healthcare | |
| | fr-FR | French (France) | 25h | Healthcare, Meetings | |
| | ja-JP | Japanese | 20h | Healthcare, Meetings | |
| | ko-KR | Korean | 18h | Healthcare | |
| | yue-HK | Cantonese | 15h | Healthcare | |
| | de-DE | German | 14h | Healthcare | |
| | it-IT | Italian | 12h | Healthcare | |
| | es-ES | Spanish (Spain) | 10h | Meetings | |
| | en-AU | English (Australia) | 8h | Call Centers | |
| | en-IN | English (India) | 6h | Call Centers | |
| | hi-IN | Hindi | 5h | Call Centers | |
| | fr-CA | French (Canada) | 4h | Call Centers | |
| | es-AR | Spanish (Argentina) | 4h | Meetings | |
|
|
| --- |
|
|
| ## Domain Breakdown |
|
|
| ### Healthcare (205 hours) |
| Medical conversations across specialties: general practice consultations, mental health sessions, dental discussions, specialist referrals. Recorded with healthcare professionals and patients discussing real (simulated) medical scenarios. |
|
|
| **Subdomains:** General Medicine, Mental Health, Dentistry, Cardiology, Pediatrics, Geriatrics |
|
|
| ### Meetings (81 hours) |
| Business and professional conversations: team discussions, project planning, client calls, interview scenarios. Natural back-and-forth dialogue with interruptions, crosstalk, and real conversational dynamics. |
|
|
| **Subdomains:** Business Strategy, Project Management, Sales Calls, HR Interviews |
|
|
| ### Call Centers (19 hours) |
| Customer service scenarios: support calls, complaint handling, booking and scheduling. Simulated but realistic call center interactions with appropriate pacing and turn-taking. |
|
|
| **Subdomains:** Technical Support, Customer Service, Booking, Complaints |
|
|
| --- |
|
|
| ## Transcription Details |
|
|
| Every audio file includes: |
|
|
| - **Full verbatim transcript** — Including fillers, false starts, and disfluencies |
| - **Word-level timestamps** — Start and end time for every word |
| - **Speaker diarization** — Speaker labels (Speaker A / Speaker B) for every segment |
| - **Confidence scores** — Per-word confidence from ASR model |
|
|
| **Transcription Process:** |
| 1. Primary transcription via ElevenLabs Scribe v2 |
| 2. Secondary pass with Whisper Large v3 |
| 3. Human verification for samples |
|
|
| **Format:** JSON with structured segments |
|
|
| ```json |
| { |
| "segments": [ |
| { |
| "start": "00:00:01.240", |
| "end": "00:00:05.890", |
| "speaker": "Speaker A", |
| "text": "Good morning, thanks for coming in today.", |
| "words": [ |
| {"word": "Good", "start": 1.24, "end": 1.48, "confidence": 0.98}, |
| {"word": "morning", "start": 1.52, "end": 1.89, "confidence": 0.99} |
| ] |
| } |
| ] |
| } |
| ``` |
|
|
| --- |
|
|
| ## Dataset Creation Methodology |
|
|
| ### Recording Process |
|
|
| All recordings were conducted via professional remote recording platforms (Zencastr and Riverside) that capture each participant on separate audio tracks. This ensures: |
|
|
| - **Clean channel separation** — No bleed between speakers |
| - **Consistent quality** — Platform handles audio optimization |
| - **Natural conversation** — Participants in comfortable environments |
|
|
| ### Contributor Selection |
|
|
| Contributors were recruited through our verified community network of 300,000+ members. Selection criteria: |
|
|
| - Native speaker of target language |
| - Clear speech without heavy regional accents (unless specifically required) |
| - Comfortable with conversational topics |
| - Passed audio quality screening |
|
|
| ### Quality Assurance |
|
|
| Multi-stage QC pipeline: |
|
|
| 1. **Automated checks:** Duration, sample rate, channel count, SNR |
| 2. **Language verification:** Confirmed correct language via speech recognition |
| 3. **Content review:** Spot-checked for topic adherence and quality |
| 4. **Transcript validation:** Compared ASR output against audio samples |
|
|
| --- |
|
|
| ## Intended Uses |
|
|
| This dataset is designed for: |
|
|
| - ✅ Training and fine-tuning **Automatic Speech Recognition (ASR)** models |
| - ✅ **Speaker diarization** research and model development |
| - ✅ **Conversational AI** training (natural dialogue patterns) |
| - ✅ **Healthcare NLP** applications (medical conversation understanding) |
| - ✅ **Multilingual ASR** benchmarking |
| - ✅ **Speech-to-text** model evaluation |
| - ✅ Academic and commercial research |
|
|
| --- |
|
|
| ## Out-of-Scope Uses |
|
|
| This dataset is **not** intended for: |
|
|
| - ❌ Real-time, safety-critical medical diagnosis systems |
| - ❌ Biometric speaker identification without consent |
| - ❌ Training voice cloning systems without proper licensing |
| - ❌ Any application that violates applicable privacy laws |
|
|
| --- |
|
|
| ## Licensing |
|
|
| This dataset is available under a **commercial license**. |
|
|
| - **Pricing:** $65 per hour of audio |
| - **Full dataset:** ~$19,800 |
| - **Custom subsets:** Available (by locale, domain, or duration) |
| - **License type:** Non-exclusive, perpetual use license |
|
|
| To discuss licensing, contact us directly or request access through this page. |
|
|
| --- |
|
|
| ## Why UsergyAI? |
|
|
| We spent years inside the world's largest AI data companies. We saw how datasets actually get built — the shortcuts, the anonymous crowds, the "quality checks" that catch formatting errors but miss meaning. |
|
|
| Then we built something different. |
|
|
| **300,000+ verified community members.** Not anonymous contractors. Real people with real expertise who come back because we never lied to them about what a project paid or what it required. |
|
|
| **Full traceability.** Every data point traces back to a person we know. Not a user ID. A person. |
|
|
| **The [human] standard.** Because AI is only as good as its data, and data is only as good as the people who create it. |
|
|
| --- |
|
|
| ## Contact |
|
|
| For licensing inquiries, custom datasets, or questions: |
|
|
| **Swaroop** (Founder) |
| 📧 swaroop@usergy.ai |
| 🌐 [usergy.ai](https://usergy.ai) |
|
|
| --- |
|
|
| *The [human] standard for AI data.* |
|
|
| --- |
|
|
| ## Citation |
|
|
| ```bibtex |
| @dataset{usergyai_conversational_speech_2026, |
| author = {UsergyAI}, |
| title = {Global Conversational Speech Dataset}, |
| year = {2026}, |
| publisher = {Hugging Face}, |
| url = {https://huggingface.co/datasets/UsergyAI/Global-Conversational-Speech} |
| } |
| ``` |
|
|
