Don’t see a model below that matches your need? We can start with one of our base use case models and quickly — within weeks — train a tailored speech model for your use case.
USE CASES
Different Environments Call for Different Speech Recognition Models
Every customer interaction is unique but all-in-one speech recognition models don’t understand these differences. Deepgram’s models are highly accurate for your use case because we know transcribing the conversation of an automated drive-thru doesn’t have the same requirements as transcribing the contents of an earnings call.
One Size Does Not Fit All
How many times have you seen speech recognition companies claim their all-in-one solution works great for all use cases and industries? How can they possibly work great for everything? At Deepgram, we know this is not possible.
We know that each customer has unique words, jargon, and terminology they use. On top of that, some situations have a high amount of environmental noise or crosstalk that needs to be filtered or separated to get the transcription accuracy you need. We have optimized our speech models for different situations to filter out different audio, identify unique terminology, jargon, noise, and other factors specific to that use case.
Conversational AI
Created for conversational AI voicebots and for IVR applications where specific words are more important than other words to determine intent.
Earnings Calls
Created for transcribing the audio or video presentation of earnings reports and follow-on Q&A sessions. The most important aspect of this model is the financial terms that need to be transcribed.
General
This is our first and most general model that can be used for general transcription needs.
Meeting
Created for meetings that may have multiple speakers on one audio channel, crosstalk, and/or environmental noise.
Phone Call
Created for contact centers and other two-channel phone calls where each speaker is on different channels.
Voicemail
Created for voicemail transcription where this is normally just one speaker on one channel with fairly clear audio.
