quality_assurance · education · workflow
Speak cuts data labeling time by 50% and improves model accuracy by 35% with Labelbox
Speak's AI models required vast amounts of high-quality labeled data across audio, transcriptions, and video to support complex language dynamics, but managing and labeling this data at scale — previously handled through individual contractors and spreadsheets — became a significant challenge as the user base grew.
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 · Diverse data sources collected
Speak works with diverse data sources including audio clips, transcriptions, and video-based conversations.
Tools used
LabelboxOpenAI · partnerGoogle Cloud Platform (GCP)
Outcome
Speak's data labeling time was cut by nearly 50%, model accuracy improved by 35%, and model development accelerated 2x after adopting Labelbox, enabling faster release of new features and languages.
What failed first
The previous data labeling process relied on a small number of individual contractors and scattered spreadsheets, making it difficult to maintain consistency or scale data operations.
Results
Time savednearly 50%
Volume35%
Grounding & classification
Source type: vendor customer story
25 fields verified against source quotes.
conversational aispeech to textcall recordingfailure mode describedhuman review describedmetric backednamed customerproduction runtime claimedtools describedworkflow describededucationaccuracy improvementcycle time reductionthroughput increasevendor customer storydata entry opsquality assurancehuman review queuemonitor detect alert