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Building a custom Automated Voice Recognition (AVR) model using machine learning to transcribe audio recordings for applications like medical or legal transcriptions.
3x to 4x increase in transcription productivity, enabling much lower operational costs and competitive edge over rivals.
AVR model built using Training Data
AVR model optimized using Cross Validation data
AVR model accuracy evaluated on Test data
Random noise may be added to the training data to make the model more robust
E.g. Deep Bidirectional LSTM RNN trained using Connectionist Temporal Classification)
From Voice frames (e.g. Mel-Frequency Cepstral Coefficients)
