Natural Language Understanding (NLU) for Audio Requires a Highly Accurate and Fast Speech to Text Foundation

As companies consider adding voicebots to their customer service, they are experiencing the same "Achilles’ heel" with NLU voicebots as they do with chatbots.These issues are the following (the fourth one only applies to voicebots):

1. Responding with the wrong intent

2. Transferring to a human agent due to lack of confidence, when it should have understood the intent from the beginning

3. Responding when it doesn’t have to, instead of passing it on to an agent

4. Not understanding the customer and asking, “Sorry, I did not get that, can you repeat.”


The voicebot needs to understand what is said first before it can determine intent, route correctly to an agent, or answer the request with a knowledge base AI. Deepgram is built for Conversational Al and voicebots with an End to End Deep Learning approach to automatic speech recognition (ASR). This approach allows you to solve for real-time speed at <300 millisecond lag and obtain 90%+ trained accuracy.

If you want to try Bitext's training data, download this free dataset that covers the 27 most common intents and over 20,000 utterances in the Customer Service domain. 


With higher accuracy data from the ASR, you can then dig into optimizing your NLU voicebot with Bitext. Get a snapshot of your NLU voicebot performance and find the root cause of incorrect responses or mis-routing. With the Bitext and Deepgram partnership, companies can analyze and improve their entire NLU voicebot platform to either correct issues or identify weak points before product release. Then, we can help create audio and voicebot training data to improve your models and track this improvement for further optimization and quality control.

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