Voice Recording & Creation

This task captures voices in all their flavors—think “Hello” with a Scottish burr, “Thanks” in a bright chirp (e.g., “Hey” gruff and low, “Bye” soft and slow)—to give AI a human touch. Our team records these tones, crafting speech that sings with life and style.

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are essential in orchestrating the collection and production of data for Voice Recording & Creation within audio processing workflows.

We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to produce voice datasets that enhance AI’s ability to deliver natural, expressive speech.

Training and Onboarding

PMs design and implement training programs to ensure workers master recording techniques, accent diversity, and tone variation. For example, they might train teams to record “Good day” with an Irish lilt or “See ya” in a casual drawl, guided by sample clips and voice standards. Onboarding includes hands-on tasks like capturing audio, feedback loops, and calibration sessions to align outputs with AI speech goals. PMs also establish workflows, such as multi-take reviews for vocal richness.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., recording 15,000 voice samples) and set metrics like audio clarity, accent authenticity, or style variety. They track progress via dashboards, address recording issues, and refine methods based on worker insights or evolving voice needs.

Collaboration with AI Teams

PMs connect recorders with machine learning engineers, translating technical requirements (e.g., broad tonal range) into actionable recording tasks. They also manage timelines, ensuring recorded datasets align with AI training and deployment schedules.

We Manage the Tasks Performed by Workers

The recorders, voice actors, or audio analysts perform the detailed work of capturing and structuring voice datasets for AI training. Their efforts are vocal and creative, requiring performance skills and sound precision.

Labeling and Tagging

For voice data, we might tag clips as “British accent” or “upbeat tone.” In complex tasks, they label features like “raspy voice” or “slow pace.”

Contextual Analysis

Our team records speech, capturing “Cheers” with a pub vibe or “Hello” with a formal edge, ensuring AI voices fit the scene perfectly.

Flagging Violations

Workers review datasets, flagging flaws (e.g., background hum) or mismatches (e.g., wrong mood), maintaining dataset quality and expressiveness.

Edge Case Resolution

We tackle complex cases—like rare accents or emotional depth—often requiring multiple takes or escalation to voice experts.

We can quickly adapt to and operate within our clients’ audio platforms, such as proprietary recording tools or industry-standard systems, efficiently processing batches of data ranging from dozens to thousands of clips per shift, depending on the complexity of the recordings and voices.

Data Volumes Needed to Improve AI

The volume of recorded voice data required to enhance AI systems varies based on the diversity of styles and the model’s complexity. General benchmarks provide a framework, tailored to specific needs:

Baseline Training

A functional voice model might require 5,000–20,000 clips per style (e.g., 20,000 casual samples). For varied or branded voices, this could rise to ensure coverage.

Iterative Refinement

To boost quality (e.g., from 85% to 95% naturalness), an additional 3,000–10,000 clips per issue (e.g., flat delivery) are often needed. For instance, refining a model might demand 5,000 new recordings.

Scale for Robustness

Large-scale applications (e.g., virtual assistant networks) require datasets in the hundreds of thousands to handle edge cases, rare tones, or new accents. A recording effort might start with 100,000 clips, expanding by 25,000 annually as systems scale.

Active Learning

Advanced systems use active learning, where AI flags weak voices for further recording. This reduces total volume but requires ongoing effort—perhaps 500–2,000 clips weekly—to sustain quality.

The scale demands distributed teams, often hundreds or thousands of workers globally, coordinated by PMs to ensure consistency and vocal precision across datasets.

Multilingual & Multicultural Voice Recording & Creation

We can assist you with voice recording and creation across diverse linguistic and cultural landscapes.

Our team is equipped to capture and refine voice data from global populations, ensuring natural, culturally relevant datasets tailored to your specific AI objectives.

We work in the following languages: