What makes
Our Speech Data Labeling Solutions
More Cost-Efficient
- Because we enable superior AI results within a smaller budget.
Efficient Labeling
Enhance data labeling efficiency with customized confidence score models.
Challenges: To prioritize human data labeling, use your AI model to label unlabeled data, then focus human efforts on low-confidence predictions. However, AI models can be overconfident, potentially undermining this prioritization and lead to increased labeling cost.
Our solutions: provide customizable models to reliably estimate phone/word/utterance-level confidence scores, greatly improving the efficiency of your data labeling process.
Resource Saving
Require minimal GPU resources with cost-efficient label validation models.
Challenges: the scaling law suggests that large AI models, though effective for labeling, require significant resources and can be costly.
Our solutions:: rely on intelligent cross-checking of metadata, requiring little to no GPU resources compared to many open-source alternatives while maintaining high validation accuracy. This enables you to save your valuable GPUs resources.
Automated Labeling
Reduce human labeling effort by supplying cost-efficient data.
Challenges: The cost of human labeling increases as the model size grows Data breach risk due to the involvement of human labelers.
Our solutions:: By incorporating our customized pre-labeled datasets, which are closely aligned with the distribution of the client’s private data, into our semi-automated training framework, the need for extensive human labeling is significantly reduced. This not only cuts costs but also minimizes the risk of data breaches.
