Key Stats
- 50%+ — Workload reduction
- 2 weeks — Initial prototype delivered
- Encrypted — Full-scale security
- ML-trained — Custom vocabulary recognition
The Approach
The Kablamo-built DAME (Digital Asset Management Experience) was the tool to meet these priorities. The customisation ranged from custom vocabulary to high-level security practices, such as log auditing and encryption.
The initial prototype was delivered in less than two weeks, with user feedback aiding the optimisation of the finalised implementation. AWS Transcribe was tested with real interview data of different qualities and varieties — including accents, slang usage, diction, recording equipment, and file type.
The Results
The speech-to-text automation reduced administrative staff's workload by 50% or more for most audio recordings. Machine learning solutions were specially trained to recognise regular phrases and local place names, remove mutters, and apply grammar rules to common phraseology.
Looking Forward
This solution goes well beyond transcription efficiencies. It opens up a powerful machine learning-based future that can support investigative outcomes. In the near future, disparate interview data can be securely meta tagged, searched and linked as the platform continues to evolve.
