Audio & speech data teams

Audio-Visual Transcription, Diarization & Acoustic Intelligence

A dedicated team of transcribers and audio specialists, matched to your languages and dialects, who transcribe, diarize, and annotate real-world audio across accents and noisy conditions, to the timestamp.

Build Your Audio & Speech Data Team See How It Works

01Why Hugo

Managed Teams, Measured Results

150+
Languages and dialects supported
10K+
Managed specialists across the globe
98%+
Quality performance across key programs
24/7
Global coverage and support

02What the team does

Every Layer of Speech and Audio Data

Audio Signal Quality Verification
Recordings audited for noise, clipping, and voice activity boundaries, so unusable audio is caught before it enters training.
Bad audio labeled carefully is still bad training data.
Speech Recognition & Transcript Auditing
Speech-to-text output verified against the source, with missing words, phonetic errors, and code-switching corrected across accents and dialects.
A transcriber who doesn’t speak the dialect guesses, and the model learns the guess.
Speaker Diarization & Identification
Speakers identified, turns marked, and overlapping speech resolved across multi-speaker recordings.
Overlap and cross-talk are where crowd diarization falls apart.
Acoustic Event & Sound Annotation
Laughter, sighs, background sounds, and non-verbal events labeled, so models understand the room, not only the words.
Non-verbal audio needs a consistent event taxonomy, not ad-hoc tags.
Visual-Acoustic Alignment (AVSR)
Visible speakers, facial movement, and lip activity synchronized with the audio stream for speech recognition in noisy environments.
Aligning two streams takes trained hands and tooling, not a one-off task.
Transcript Formatting & Semantic Alignment
Precise timestamps, disfluencies handled to your spec, and transcripts, subtitles, and speech data structured the way your pipeline expects.
A transcript in the wrong format is a transcript you pay for twice.

03Who it’s for

Built for Teams Training Speech and Voice Models

Speech recognition, voice agents, conversational AI, media, and any product that has to understand people the way they actually talk.

Build Your Audio & Speech Data Team

Built for

Speech & ASR teams
Voice agents & conversational AI
Multimodal model training
Call center & CX analytics
Media & accessibility
Data operations leads

The team you get

Transcribers matched to your languages, from 150+ languages and dialects
Diarization and timestamp specialists
Acoustic annotators trained on your event taxonomy
A QA and calibration lead who owns your standards
A team lead who owns throughput and reporting

04How we hold quality

Quality Is a Process, Not a Promise

Every Hugo team runs on written guidelines, a calibration set, and measured agreement. These are the mechanisms behind this one.

Accents and dialects the transcriber doesn’t speak: Transcribers recruited to your language and dialect list, and tested on your audio before production.
Word errors that compound at scale: A defined word-error protocol, second-pass review on a stratified sample of every batch, and per-language accuracy reported.
Timestamps and turns that drift: Turn-level timestamp tolerances set with you and checked in QA, with diarization audited on the overlapping segments where it fails.
Disfluencies and events handled differently by each hand: A written style guide for fillers, false starts, and acoustic events, versioned, and re-calibrated when it changes.

A Team, Not a Queue

Every program gets a team lead who owns the workflow, a QA and calibration lead who owns the standards, and specialists matched to the work, trained together and kept together.

Team lead: workflow owner
QA & calibration lead: quality and standards
Specialists matched to workflow complexity
90%+ team retention across programs

05How it works

From First Call to a Live Team

Hugo sources, trains, assembles, and manages the team around your workflow, tools, and quality bar, then keeps managing it.

Define the Workflow and the Team
We map the task, the tools, the rubric or taxonomy, the volume, and the quality bar with you.
Recruit and Vet the Specialists
Hugo sources and tests the people the work needs, from 10K+ managed specialists across 100+ specializations.
Train on Your Tools and Edge Cases
The team learns your platform, your guidelines, and a library of edge cases, and is tested on them.
Calibrate Before Production
A calibration set, agreement measured, and the guidelines tightened until the team converges.
Launch, Manage, and Scale
The team goes live with reporting per batch. Hugo manages performance, re-calibration, and scale from there.

Built with Speed, Designed for Quality

We can stand up specialized AI ops teams faster than traditional hiring — typically within days to weeks, depending on the workflow and complexity.

Getting Faster

We’re continuously developing pre-trained agents and reusable workflows to reduce the ramp time as we scale.

Build Your Audio & Speech Data Team

06Managed team vs crowd

What a Dedicated Team Changes

Unmanaged Crowds

Fragmented, transactional, and hard to calibrate.

Accent coverage by chance
Timestamps as close as the tool allows
Style decided per transcriber

Hugo AI Operations Teams

Connected, managed, and built for long-term performance.

Transcribers recruited to your dialect list
Timestamps to the tolerance you set
One style guide, calibrated and audited

Need a more reliable human operations model?

Build a dedicated AI operations team aligned to your workflow.

Build Your Audio & Speech Data Team

07Trust & security

Security and Operational Control for Sensitive AI Workflows

AI operations involve sensitive data, from proprietary outputs and source code to customer information. Hugo provides secure, quality-controlled workflows that protect data without sacrificing performance.

08FAQs

Audio & Speech Data FAQs

Answers to common questions about how Hugo builds and manages a dedicated audio and speech data team.

Marking who spoke when in a recording: assigning each segment to a speaker, marking the turns, and resolving overlap. It is what lets a model follow a conversation rather than a stream of words.
Teams are built across 150+ languages and dialects, matched to the list you give us, including code-switched speech.
Against a word-error protocol agreed with you, with second-pass review on a sample of every batch and per-language accuracy reported, plus timestamp tolerances and diarization checks on overlapping speech.
Yes. Signal quality is verified first, so unusable audio is flagged rather than guessed, and transcribers are tested on your conditions before production.
Yes. The team works in your platform and delivers transcripts, subtitles, and speech data in the format and timestamp convention your pipeline expects.
Typically within days to weeks, depending on the languages and the tooling. Hugo recruits the transcribers, trains them on your style guide, and calibrates them before the first batch.