Scripted speech collection
Prompted sentences, commands, keywords and targeted utterances across speakers, devices, accents and delivery styles.
Build speech & voice AI
Voice AI starts with real-world speech.
Collect, transcribe, annotate and evaluate speech for ASR, voice assistants, TTS, wake words, conversational systems and multilingual voice AI.
Speech quality labsession / 00:14:28
REFERENCE TRANSCRIPT
00:04 Welcome to the evaluation session.
00:09 Let’s test the voice assistant in a noisy room.
00:14 Intent recognized · confidence tracked
WERtrackedNoiseofficeLocaleen-GB
Speech-firstCapture real variation
Signal-awareKeep audio context
Human-listenedValidate the experience
Built around the real work
Capabilities that move a model from promising to useful.
Choose the parts of the data and quality workflow that match the behavior, market or modality you are building for.
BUILD
Designed for the next decision, not the last task.
Every capability is shaped around downstream use: what the model needs to learn, what reviewers need to judge and what your team needs to ship with confidence.
Talk through your workflow01
Conversational speech
Spontaneous or scenario-guided dialogues for voice agents, call flows, intent recognition and context-aware ASR.
Environmental & device variation
Record speech in homes, offices, vehicles, outdoors or other target environments with realistic channels and background conditions.
Transcription & speech annotation
Human-reviewed transcripts, timestamps, speaker labels, segmentation, intent, entities and pronunciation notes.
ASR evaluation
Measure error, semantic accuracy, punctuation, diarization, timestamps, domain terminology and error patterns by locale.
TTS & voice evaluation
Human ratings and pairwise tests for naturalness, intelligibility, pronunciation, prosody, emotion and speaker consistency.
Speech slice reviewSignal preserved
Acoustic conditionoffice / near-field
Scores explain what to improve.Words, timing, acoustics and interaction remain connected.
Different voice products need different scorecards
From raw voice capture to model evaluation.
A strong speech program combines collection conditions, high-quality transcription, metadata and human listening tests around the target use case.
- WER / CER, semantic match and domain vocabulary
- Naturalness, intelligibility, prosody and speaker consistency
- Intent, interruption handling, multi-turn context and completion
The operating model
A speech program that connects data and evaluation.
Target the next data batch at the accents, environments and behaviors where performance is weakest.
Define
Map users, languages, accents, devices, acoustic environments and success metrics.
Collect
Capture representative speech and test whether the protocol creates the right variation.
Transcribe
Create reference transcripts, metadata and task-specific annotations with calibrated QA.
Evaluate
Run ASR, TTS or voice-agent tests across target slices and identify failures.
Target
Collect or curate new examples for the conditions and behaviors that need improvement.
Quality that holds up in production
Keep the signal connected to the experience.
Speech quality can fail through words, timing, speakers, acoustics or interaction. Preserve those dimensions so the score explains what to improve.
- Speaker, language, environment, device and channel metadata
- Human-reviewed transcripts with timestamps and segmentation
- Listening tests, error taxonomies and slice-level analysis
ASRWER / CER, semantic accuracy, punctuation, timestamps and diarization
TTSNaturalness, intelligibility, pronunciation, prosody, emotion and preference
Voice agentsIntent, turn taking, interruptions, multi-turn context and completion
Wake wordsFalse accepts, false rejects, confusables, noise, distance and device variation
What arrives at the end
Deliverables your team can use.
Clear outputs, stable structure and the context needed to move the work into training, evaluation or production.
01Scripted and spontaneous speech data
02Human-reviewed transcripts
03Speaker, language and acoustic labels
04Timestamps and segmentation
05Intent, entity and slot annotations
06ASR / TTS evaluation results
07Voice-agent and wake-word test cases
08Error taxonomy by locale, device and environment
Where it fits
Automatic speech recognition
Measure words, meaning, punctuation, timing, speakers and vocabulary.
Text-to-speech
Evaluate naturalness, pronunciation, pacing, emotion and preference.
Voice assistants
Test intent, interruption handling, multi-turn flow and task completion.
Speech translation
Preserve meaning from transcription through target-language voice experience.
Start with the outcome
Build voice AI for the conditions your users actually create.
Tell us the languages, accents, devices, environments and model goal. We’ll design the speech collection, transcription and evaluation workflow around them.