TazQ
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03 / Capability

Help models connect what they see and hear.

TazQ provides multimodal and speech AI data: transcribed and labelled conversational audio for speech recognition, expert review of synthetic voices, and image and video annotation for vision-language models. Native speakers and trained annotators add the context models need to understand people across languages, accents and situations.

Plan a multimodal project See how it works

Updated ASR · TTS · VLM · Multilingual audio

Definition

What is multimodal and speech AI data?

Multimodal and speech AI data is training and evaluation data that pairs language with sound, images or video. It includes audio with accurate transcripts and speaker labels for speech recognition (ASR), human judgments of synthetic speech for text-to-speech (TTS), and images or videos with descriptions, questions and labels for vision-language models (VLMs).

Why it matters

Context lives between the senses.

Models need examples that tie what is said to what is shown, as people really experience it.

01

Real speech is messy.

Accents, overlapping voices, background noise and code-switching are everyday speech, and models trained only on clean audio struggle with them.

02

Quality is perceptual.

Whether a synthetic voice sounds natural, or a caption truly matches a scene, is a human judgment that metrics alone can’t settle.

03

Languages need native ears.

Transcribing and judging speech well takes native speakers who understand regional variation, slang and cultural context.

Free: the Multimodal & Speech AI readiness checklist

Seven decisions for speech, audio and multimodal data, from which voices to include to how quality will be measured.

How TazQ does it

From raw signal to shared meaning.

Every multimodal project follows four steps, shaped around what your model must perceive.

  1. 01

    Specify languages, voices and scenes.

    Define the languages, accents, speakers, environments and visual content your model must handle.

  2. 02

    Collect or curate the media.

    Record new audio or video with consenting participants, or curate your existing media against the brief.

  3. 03

    Annotate with native expertise.

    Native speakers and trained annotators transcribe, label speakers and emotion, caption images and mark actions in video.

  4. 04

    Review, measure and deliver.

    Layered review and agreement checks run on every batch before delivery in the formats your pipeline uses.

Deliverables

What you receive.

Formats adapt to your pipeline. These are the data types multimodal teams ask for most.

Multimodal & Speech AI data types, what each includes, and typical formats
DataWhat it includesTypical formats
Transcribed speechConversational and read audio with accurate, time-aligned transcripts and speaker labels.Audio (e.g. WAV) with JSON or SRT
Speech annotationsEmotion, intent, non-speech events and language switches, marked on the timeline.Time-aligned JSON labels
TTS evaluationsNative-speaker ratings and comparisons of synthetic voices for naturalness and accuracy.Scored ratings with comments
Image–language dataDetailed captions, visual question–answer pairs and region labels for images.JSONL with image references
Video annotationsAction labels, temporal segments and dense descriptions of what happens in each clip.Segment JSON with timestamps
Voice-agent evaluationsExpert-scored conversations with spoken assistants, noting misunderstandings and recovery.Scored transcripts with labels

Want to see one?

Request an anonymised sample of any deliverable. A specialist will share a relevant example where available.

Use cases

Where teams use it.

01Speech recognition

Diverse, accurately transcribed audio that helps ASR models understand more people in more places.

02Natural-sounding voices

Native-speaker evaluation that shows where synthetic speech sounds wrong and how to fix it.

03Vision-language assistants

Captions and visual Q&A that teach models to describe and reason about what they see.

04Voice agents

Real conversations and expert review for assistants that must listen, understand and respond in real time.

Quality & experts

Quality starts with people.

Native speakers, prepared

Annotators work in their own languages and are prepared through TazQ Academy on each project’s guidelines.

Explore TazQ Academy

Consent by design

Participants know how their voices and images will be used, and personal details are removed before delivery.

Layered review

Batches pass automated checks and expert review, with agreement measured and guidelines refined along the way.

FAQ

Questions, answered.

01

What is ASR training data?

ASR (automatic speech recognition) training data is audio paired with accurate, time-aligned transcripts. Diverse speakers, accents and recording conditions help a speech model understand more people reliably.

02

How do you evaluate text-to-speech voices?

Native speakers listen to synthetic speech and rate or compare it for naturalness, pronunciation, rhythm and emotion, noting specific errors so they can be fixed.

03

What is VLM data?

VLM (vision-language model) data pairs images or video with language: captions, questions and answers, and labels for regions or actions. It teaches a model to connect what it sees with words.

04

Which languages and accents can you cover?

Projects are scoped around the languages and regional varieties you need, with native speakers for each. Brazilian Portuguese is a particular strength.

05

How is consent handled for voice and video recordings?

Participants give informed consent for how their recordings will be used, and personal details that are not part of the task are removed before delivery.

06

How do we start a multimodal project?

Share the languages, media types and volume you need, and your target format. A pilot batch then validates guidelines and quality before scaling. You can outline this in the project brief below.

Related capabilities

All capabilities
04 /↗

Physical AI

Egocentric recordings, sensor annotation, and human demonstrations that connect embodied systems to the complexity of the physical world.

  • Robotics
  • LiDAR
  • World models
02 /↗

Agentic AI

Human-led trajectories, realistic task environments, and deep evaluation for agents that need to act, adapt, and complete real work.

  • Trajectories
  • RL environments
  • Evaluation

Start your Multimodal & Speech AI project

Let’s build
what comes next.

A better model starts with a clearer question.
What do you want yours to understand?

For AI teams

Scope your project in about a minute. A specialist reads every brief and replies with next steps.

For human expertsExplore TazQ Academy
YOUR PROJECT, IN FOCUS
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