Voice may have a lot of errors in the speech, how does Agara deal with this?

  • Agara uses robust natural language understanding systems that learn to accommodate for errors based on context
  • Agara builds conversations around clarifying mistranscribed information, keeping in mind that the UX should still be smooth – similar to how a good agent would handle things they have misheard.

Noise environments

We have been dealing with noise-environments for large clients as is typical with customer support calls. There are a few ways we deal with them 

  • Build powerful language models on client-specific data to leverage context and fill in noisy places. This works for cases where things can be guessed from context. 
  • Build specific SLU units meant for very specific important entities and intents that are trained with noisy data. There are 2 ways we do this:
    • Data augmentation techniques to artificially add noise to normal speech. This helps but doesn’t go far enough.
    • Train the models on real customer calls where customers are calling from noisy environments.

Models are trained on a regular basis looking at areas where improvements are needed. They are also additionally trained to manage the specific needs of clients.

A strong source of data and improvements comes from recorded customer conversations provided to us by our clients. These conversations are the closest data to what the voice bot will encounter and are used in additional training of the models. It is important to note that no client data is ever shared with anyone in any form for any reason.