Voice AI needs real human speakers in controlled environments with precise demographic coverage. Crowd platforms deliver inconsistent recording quality, unverified speaker metadata, and zero control over accent or dialect distribution. Teams spend months cleaning noisy datasets that still lack the speaker diversity their models need.
Key Benefits
Structured. Defensible. Scalable.
Verified Speakers, 50+ Languages
Every speaker is identity-verified with documented demographics: language, accent, dialect, age, and gender. Accurate metadata, no self-reported guesses.
Studio-Quality Standards
Recordings follow defined audio specs: sample rate, bit depth, noise floor, and clipping. Files failing requirements are rejected before reaching your pipeline.
Demographic Precision at Scale
Need 500 hours of Hindi female speakers (25-35) with Rajasthani accents? We source exactly that. Control distribution, dialect coverage, and speaker diversity.
Privacy and Consent Built In
Every speaker provides verified informed consent. Data handling follows GDPR and regional privacy requirements. Usage rights are clear from day one.
How It Works
Structured. Defensible. Scalable.
Define Your Voice Data Spec
Tell us what you're building and we'll define the recording spec together: languages, accents, demographics, audio format, utterance types, and volume.
We Source and Record
Verified speakers record to your exact specs in controlled environments. Every speaker's identity and demographics are verified before a session starts.
QC, Annotate, Deliver
Every recording passes audio QC: noise, clipping, and transcript alignment. Peer review plus centralized QC. Delivered in your preferred format with full metadata.
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03Capabilities
Every Modality. Every Language. Every Domain.
Voice Collection Types
- Read speech: scripted prompts, passages, and phonetically balanced sentences
- Spontaneous and conversational speech: natural dialogue and multi-turn conversations
- Emotional speech: anger, joy, sadness, surprise, and neutral for sentiment models
- Wake word, hotword, and command recordings for voice interfaces
- Multi-speaker dialogue and noisy environment recordings for robust ASR
- Whispered, shouted, and varied speaking style recordings
Languages and Coverage
- 50+ languages including major global languages and low-resource languages
- Indic languages: Hindi, Tamil, Telugu, Kannada, Malayalam, Bengali, Marathi, and more
- Dialect and accent coverage within languages with native speaker verification
- Multilingual code-switching recordings for bilingual speech models
- Configurable audio specs: 16kHz to 48kHz, 16 or 24-bit, WAV, FLAC, or MP3
- Full demographic controls: age, gender, accent, dialect, and regional distribution
Use Cases
Built for Teams
Building Voice Data Collection
Voice AI and TTS Companies
Studio-quality recordings for TTS model training, voice cloning, and synthesis across multiple languages and speaking styles.
ASR and Speech Recognition Teams
Large-scale transcribed speech with accent diversity, noisy environments, and domain-specific vocabulary for production ASR.
Your voice model deserves data from real, verified speakers.
Tell us the languages, demographics, and recording specs. We'll return with a collection plan and sample recordings within 48 hours.
Got questions? Check our FAQs
Voice data collection for AI is the process of recording human speech under controlled conditions to create training datasets for speech models. This includes read speech, spontaneous conversations, command utterances, and emotional speech, recorded by verified speakers with documented demographics in environments that meet defined quality standards.
Humyn Labs supports 50+ languages including major global languages and low-resource languages, with particular depth in Indic languages like Hindi, Tamil, Telugu, Kannada, Malayalam, and Bengali. We also support dialect-specific and accent-specific collection within languages.
Every recording passes audio-specific QC: automated checks for noise floor, clipping, silence ratio, and sample rate, followed by human verification for transcript accuracy and speaker identity. Our multi-layer process combines peer review with a centralized QC team.
Voice data collection is sourcing and recording new speech audio from human speakers. Audio annotation is labeling existing audio with transcriptions, speaker labels, timestamps, and emotion tags. Humyn Labs provides both in a single pipeline.
A pilot project of 50 to 100 hours in one or two languages typically takes two to four weeks. Large-scale collections of 1,000+ hours across multiple languages run two to six months. We deliver in milestones so you can start training on early batches.
Public datasets like LibriSpeech and Common Voice skew toward English, lack demographic diversity, contain inconsistent quality, and have restrictive licensing. Production voice AI needs custom datasets matched to your language, accent, demographic, and quality requirements.