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Architecture

The Corti speech recognition pipeline is based on a combination of model architectures. The main workhorse is an encoder-decoder architecture with byte pair encoding (BPE), similar to Whisper. There is also a connectionist temporal classification (CTC)-based architecture that works in tandem with the encoder model. The combination of those two modeling paradigms provide the following benefits:
  • Reduce potential hallucinations
  • Improve speed of predictions
  • Configure latency to balance response time and accuracy
  • Make it easy to integrate with the system

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Finetune

Model and architecture finetuning happens in two ways:
  1. Decoder training: Language modeling/terminology learning
  2. Encoder training: Auditory model learning
As a starting point, are used as baseline parameters upon which various methodologies are employed to refine the parameters of other models.

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Evaluate

Different data sets are used for model finetuning and training than for validation. Assessment methodologies include, but are not limited to the following:
  • Word error rate (WER)
  • Character error rate (CER)
  • Medical term accuracy rate
  • Levenshtein distance
See some of our supporting research here: