Text-to-Speech (TTS) Training Data

This task crafts voices from scratch—think “Welcome” recorded in a warm tone or “Book” phonetically split as “b-oo-k” (e.g., “Hi” with a smile, “Yes” crisp and clear)—to make AI talk like us. Our team curates these sounds, shaping TTS into smooth, lifelike speech.

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are crucial in orchestrating the collection and annotation of data for Text-to-Speech (TTS) Training Data within audio processing workflows.

We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to produce TTS datasets that enhance AI’s ability to generate natural, human-like voices.

Training and Onboarding

PMs design and implement training programs to ensure workers master voice recording, phonetic breakdown, and linguistic tagging. For example, they might train teams to record “Hello” with a friendly lilt or annotate “Cat” as “k-a-t,” guided by sample audio and TTS standards. Onboarding includes hands-on tasks like capturing speech, feedback loops, and calibration sessions to align outputs with AI voice goals. PMs also establish workflows, such as multi-step reviews for vocal clarity.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., curating 15,000 TTS samples) and set metrics like intonation accuracy, phonetic fidelity, or voice diversity. They track progress via dashboards, address recording flaws, and refine methods based on worker insights or evolving TTS needs.

Collaboration with AI Teams

PMs connect curators with machine learning engineers, translating technical requirements (e.g., expressive range) into actionable dataset tasks. They also manage timelines, ensuring curated datasets align with AI training and deployment schedules.

We Manage the Tasks Performed by Workers

The recorders, annotators, or speech analysts perform the detailed work of collecting and structuring TTS datasets for AI training. Their efforts are auditory and linguistic, requiring vocal skill and precision.

Labeling and Tagging

For TTS data, we might tag clips as “cheerful” or “neutral.” In complex tasks, they label phonemes like “th” or “short vowel.”

Contextual Analysis

Our team shapes speech, recording “Good day” with upbeat flair or breaking “Apple” into sounds, ensuring AI voices feel real and fluid.

Flagging Violations

Workers review datasets, flagging issues (e.g., flat tone) or errors (e.g., wrong phoneme), maintaining dataset quality and authenticity.

Edge Case Resolution

We tackle complex cases—like rare phonetics or emotional shifts—often requiring native speakers or escalation to speech experts.

We can quickly adapt to and operate within our clients’ audio platforms, such as proprietary TTS 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 annotations.

Data Volumes Needed to Improve AI

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

Baseline Training

A functional TTS model might require 5,000–20,000 recorded clips per voice style (e.g., 20,000 narration samples). For varied or expressive outputs, 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., stiff delivery) are often needed. For instance, refining a model might demand 5,000 new recordings.

Scale for Robustness

Large-scale applications (e.g., multilingual assistants) require datasets in the hundreds of thousands to handle edge cases, rare intonations, or new styles. A curation 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 outputs 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 Text-to-Speech (TTS) Training Data

We can assist you with TTS training data creation across diverse linguistic and cultural landscapes.

Our team is equipped to record and annotate speech data from global voices, ensuring natural, culturally relevant datasets tailored to your specific AI objectives.

We work in the following languages: