Harnessing Wild Data for High-ROI Speech R&D — Our ICASSP 2024 Spotlight
Our Founder & CEO Wei Chu's ICASSP 2024 spotlight talk on why traditional speech-service builds are low-ROI — and how filtering wild data + auto-labeling private data with large open-source speech models flips the equation.
At IEEE ICASSP 2024 in Seoul, our Founder and CEO Wei Chu delivered a spotlight talk on the shift that's making enterprise speech R&D dramatically cheaper: stop paying for human labeling and off-the-shelf datasets, start harnessing wild data and open-source foundation models.
View the talk on the ICASSP 2024 program →
Why traditional speech-service builds are low-ROI
Every enterprise building voice AI faces the same three tempting shortcuts. Each has a hidden ROI ceiling:
Call cloud APIs Fast to prototype, tiny R&D cost. But not customizable to your product, unsatisfying performance on your specific data, expensive at scale, and every request is a potential data-breach vector.
Buy from vendors Cheaper than DIY on paper. In practice: big bills from data + system vendors, sub-par performance because of the intra-company communication overhead, and still a data-breach risk.
DIY like a tech giant (Alexa, Siri, Google Voice Search) In-house stack, full data safety — and a five-year, tens-of-millions investment in R&D team, data curation, and infrastructure before you ship.
The other "cheap" alternatives fare no better. Overseas contract teams, off-shelf simulated datasets, or acquiring a struggling startup all sacrifice ROI to save cash.
The high-ROI alternative — stand on the shoulders of large speech models
Two shifts underpin Olewave's approach:
Ride the scaling law. Since 2022, the tech giants have been open-sourcing CC-BY large speech models — Whisper, SeamlessM4T, and more. You no longer need to reinvent the wheel. Fine-tune a foundation model to your niche instead of training from scratch. That eliminates most of the R&D-team, data-curation, and training-infrastructure spend.
Filter wild data and auto-label private data. Filtered wild data — validated transcriptions, timestamps, and topic tags produced by an AI-driven labeling system — is used to fine-tune a domain model. That domain model then labels your proprietary data with confidence scores. No human labelers. No data leaving your infrastructure. No breach risk. And no bills for pre-simulated datasets.
Our four-step bespoke labeling pipeline
- Jump Start — select wild data from the same domain as the client's, and fine-tune a domain model from a large speech / language foundation model.
- Auto Label — the domain model labels the client's proprietary data with per-word and per-utterance confidence scores. Human labelers are not needed — so there is also no data-breach risk.
- Iteratively Learn — compile a set of labeled private and public data, fine-tune the domain model again, then re-label the private data with the improved model. Repeat until quality plateaus.
- Good to Go — stop iteration when quality is sufficient. We license our Tycho SDK so you can keep labeling and building on your own infrastructure.
Why this matters
- Supervised training of domain-specific models without human labelers — the biggest recurring cost in enterprise speech projects.
- No data breach risk — private data never leaves your environment.
- Significantly lower labeling cost — models do the work; humans review edge cases.
- Same fine-tuning workflow scales to new domains, languages, and use cases as your product matures.
Talk to us
If you're evaluating build-vs-buy for a voice AI product, or trying to reduce data-labeling spend on an existing one, we'd love to compare notes.
Email [email protected] or reach out via the website — we'll respond within one business day.