data_entry_ops · education · workflow
super.AI accelerates audio data annotation to scale Lalilo's speech recognition dataset
Lalilo needed to build and annotate a large corpus of children's speech recordings from scratch to train a mispronunciation-detection model, but almost no annotated corpora of children's recordings existed and their internal annotation process could not keep up with the volume required.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Lalilo seeks annotation partner
Lalilo's internal annotation turnaround time is not sustainable for the volumes required and they begin looking for a partner.
Tools used
Humans & AI solution
Outcome
Lalilo significantly scaled their dataset of children's speech recordings and improved the accuracy needed to train their speech recognition system, with super.AI delivering annotated data at a significantly faster turnaround time.
Results
Time savedsignificantly faster turnaround time
Volumehigher quality of accuracy
Grounding & classification
Source type: vendor customer story
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metric backednamed customertools describedvendor confirmedworkflow describededucationaccuracy improvementcycle time reductionthroughput increasevendor customer storydata entry opshuman review queue