Train accurate, responsive, multilingual voice assistants with high-quality wake word datasets recorded by verified speakers across 100+ languages, accents, and environments.
Delivered in 2–4 weeks with 99% verified accuracy and full demographic diversity, trusted by Fortune 500 companies.
We've delivered high-quality speech datasets for enterprise AI teams, startups, and product innovators, powering real-world voice assistants in 20+ markets.
words are short, often just two seconds long, but they’re where every voice experience begins. When a user says “Hey Ava” or “OK FutureBee,” your system has a split second to recognize the intent and respond. If it misses that moment, nothing else matters. And if it activates without being called, it risks draining battery, violating privacy, and frustrating users, sometimes all at once.
A wake word speech dataset is a curated collection of real recordings where verified contributors speak your custom activation phrase, across diverse accents, age groups, genders, recording environments, and devices. These datasets help train your AI model to know exactly when it’s being addressed and just as critically, when it’s not.
But not all datasets are built for the real world. Generic wake word datasets often lack diversity, quality, and contextual noise variation needed for production-grade accuracy. The result? False activations. Missed cues. Inconsistent performance across your user base.
At FutureBeeAI, we believe your wake word detection model deserves better, because it sets the tone for everything that follows.
$11.2 Billion Voice Assistant Market by 2026
✦
The global voice assistant market is projected to quadruple from $2.8B in 2021 to $11.2B by 2026, growing at an impressive CAGR of 32.4%.
--MarketsandMarkets
52% of Users Rely on Smart Speakers Daily
✦
Over half of smart speaker owners use their devices almost every day as part of their regular routines.
--Yaguara
53% Report Weekly False Activations
✦
In a short survey of 328 users, more than half reported experiencing unintended wake word triggers at least once a week.
--Vocalize AI
Even the smartest wake word model will underperform if the data doesn’t match the users.
These are the most common pitfalls we’ve seen in wake word dataset usage and why custom collection is often the missing piece.
A model trained on just one region or accent can't generalize across global users. You need speakers who reflect your real-world audience.
Quiet room recordings don't prepare your model for noisy homes, car cabins, or open spaces. Wake words must be tested in the wild.
Blurry, clipped, or reverberant audio lowers model confidence. Every sample must pass technical QA to be usable in production.
Training without knowing speaker age, device, or environment limits your ability to fine-tune. Good data always comes with rich context.
Even great OTS datasets can’t cover every custom use case. Brand-specific phrases, accents, or regulatory needs often require custom collection.
Many wake word datasets lack transparent contributor consent, or have unclear sourcing practices. This creates legal and ethical risks for production use, especially at scale.
Imagine a Dataset Built to Avoid Every One of These Pitfalls.
We’ve worked with voice AI teams across industries, from automotive to smart devices, helping them overcome the same wake word challenges you’re facing.
Whether it's missed activations, false positives, or inconsistent performance across accents, we solve these at the source with real voices, real-world recordings, and structured, high-quality data.
Train your model on your brand-specific wake words, not just generic triggers.
Reach real-world accuracy by including regional, age, and gender variation in every dataset.
Recordings in quiet rooms, noisy homes, moving vehicles, across varying speaking paces and microphone distances — tailored to your use case.
Every sample comes tagged with speaker, device, language, and acoustic environment.
Get production-ready wake word datasets in just 2–4 weeks, with full QA and documentation.
Need ultra-clean audio for low-noise use cases? We can collect wake words in studio settings with consistent acoustics and high SNR, while preserving speaker and language diversity.
Your wake word model needs to perform in homes, vehicles, factories, and across diverse users. We help you collect data that reflects your actual use case, not just generic prompts.
✦
Wake word + non-wake word command design
✦
Multi-language and code-switched phrase support
✦
Phonetic variants and hard negatives
✦
Indoor, outdoor, in-vehicle, and reverberant environments
✦
Accent, age, gender, and speech rate diversity
✦
Quiet, low-SNR, and noisy condition balancing
✦
Sample rate, bit depth, and silence thresholds customizable
✦
Wake word position flexibility (start, middle, natural insertion)
✦
Close and distance recording collections
This isn't just clean data, it's impact-ready data. Built to help your wake word models perform better where it matters: with real users, real accents, and real-world noise.
✦
Lower false acceptance rates using hard-negative command coverage
✦
Reduce false rejections with phonetically diverse speaker data
✦
Measure latency across noise levels, devices, and speech rates
✦
Test wake word accuracy in far-field, near-field, and reverberant setups
✦
Evaluate performance during overlapping or background speech
✦
Optimize across environments: homes, cars, public spaces, factories
✦
Tune detection sensitivity with silence gaps and varied phrase positions
✦
Track performance improvements through structured benchmark support
Most teams focus on surface-level specs like speaker count, format, total hours. But real-world performance isn't built on spreadsheets.It's built on nuanced, real-world data.
A truly high-quality wake word dataset isn't just diverse or clean, it reflects the way people actually speak, in the environments they live in, through the mics they use, with all the imperfections that come with it.
That means accounting for acoustic edge cases, pronunciation drifts, noise overlays, and the micro-patterns that your model has to decode in real time.
We've studied these patterns and we build them in from day one.
Not all users begin cleanly on cue. We preserve natural timing, including soft lead-ins and trailing speech, to mimic true user behavior.
A model trained only on near-field recordings breaks in the wild. We capture variability in mic distance and angle as it happens in real use.
US English isn't one sound. Neither is Hindi or Korean. We include regional, generational, and urban-rural variations often lost in basic labels.
Some users say "Hey Ava," others say "Heyvaa." We capture inflections, reductions, and blends your model must learn to understand.
Wake words are whispered, shouted, repeated, mumbled. We include emotional tone and effort variation, because real users aren't consistent.
Even slight over/under-recording of wake phrases can train inconsistency into your model. We detect and reject them early.
Hear from industry leaders who have transformed their AI models with our high-quality data solutions.
"We partnered with FutureBeeAI to source high-quality data for training our wake-up word (WUW) and Biometrics models. The dataset was diverse, accurately labeled, and well-suited for our use case. Their team was responsive and professional, ensuring timely delivery and addressing all our needs. The data significantly improved our model's accuracy and robustness across various scenarios. Highly recommended for anyone looking for reliable training data."
"What stood out most was how easy it was to work with the team. We had a complex wake word setup involving multiple dialect groups, and FutureBeeAI adapted quickly, even mid-project. The metadata coverage was excellent — clean, structured, and made downstream integration seamless. No over-promising, just consistent delivery and clear communication. It felt like working with an extension of our own data team."
Let’s collect the right wake word dataset, built for your users, devices, environments, and use cases. Fast, accurate, fully verified, and model-ready in weeks.
