About this service
Kuinbee provides end-to-end custom voice and speech data collection for Text-to-Speech (TTS), speech AI, voice assistants, conversational AI, and related machine learning applications. We support projects ranging from targeted single-language voice collections to large-scale multilingual speech datasets involving multiple speakers, accents, demographics, recording environments, and delivery specifications. Our service can include speaker recruitment and screening, script and prompt preparation, remote or studio-based recording, consent management, recording supervision, audio quality validation, transcription, segmentation, metadata generation, annotation, and final dataset packaging. Collections can be designed around buyer-defined requirements such as language, locale, accent, age group, gender distribution, speaking style, emotion, recording environment, microphone specifications, sample rate, bit depth, duration per speaker, utterance length, and total required hours. Typical engagements include TTS training datasets; multilingual voice datasets; regional and accented speech collection; read and prompted speech; conversational speech; expressive and emotional speech; professional voice recordings; speaker-specific datasets; voice assistant datasets; speech model evaluation datasets; and custom speech collection programs. Buyers can provide their own scripts and collection protocol, or Kuinbee can assist in structuring prompts, speaker cohorts, recording guidelines, metadata schemas, and quality criteria. Each engagement is scoped according to language availability, required recording hours, speaker profile, technical specifications, geographic coverage, and delivery timeline.
Collection methods
Data types
Industries
Geographic coverage
Languages
Delivery formats
Typical deliverables
Typical delivery can include recorded voice files in WAV, FLAC, MP3, or buyer-specified audio formats; one audio file per utterance or buyer-defined segmentation structure; corresponding prompt or transcript text; unique utterance and recording IDs; speaker metadata based on agreed attributes; language, locale, accent, and dialect metadata; recording duration and technical audio metadata; sample rate, bit depth, channel, and codec information; recording session and device metadata where required; consent and participation records where applicable; dataset manifests in CSV, JSON, Excel, or Parquet; file-level quality status and validation information; annotation or linguistic metadata where included in scope; dataset documentation and collection methodology; and a delivery summary with total speakers, utterances, hours, languages, and accepted or rejected recordings. Dataset schemas, naming conventions, folder structures, metadata fields, and delivery specifications can be customized to match the buyer's ingestion and model-training pipeline.
Quality assurance
Every collection can be validated against project-specific acceptance criteria before final delivery. Our QA workflow can include automated and human review of audio duration and file integrity; sample rate, bit depth, codec, and channel configuration; clipping, distortion, excessive noise, silence, and corrupted recordings; signal quality and recording consistency; prompt-to-audio correspondence; missing, duplicated, or incorrectly named files; duplicate utterances and duplicate submissions; transcript accuracy; pronunciation and linguistic correctness; language, accent, and speaker eligibility; metadata completeness and consistency; speaker and recording distribution against project quotas; segmentation and utterance boundaries; and file-to-manifest matching. Collections may use multi-stage validation consisting of automated technical checks, linguistic review, sampling, and manual quality assessment. Recordings that fail agreed acceptance thresholds can be rejected, recollected, or corrected before final delivery. Buyer-defined QA thresholds and acceptance criteria can also be incorporated into the collection workflow.
Compliance and privacy
Voice data collection is conducted according to the consent, privacy, security, and usage requirements defined for each engagement. Where applicable, participants are informed about the purpose of the collection, intended use of their recordings, applicable usage rights, and data handling requirements before participation. Projects can incorporate explicit participant consent; usage and licensing agreements; defined data retention requirements; personally identifiable information minimization; controlled access to collected data; secure data transfer and storage; participant ID pseudonymization; separation of identifying information from dataset records; project-specific confidentiality requirements; geographic or jurisdiction-specific collection restrictions; and buyer-defined privacy and compliance protocols. Additional consent language, release documentation, security controls, and compliance requirements can be incorporated based on the project's geography, application, and intended use.