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Deepgram series models are able to analyze speech in real time, picking up things like microphone noise profiles and transmission protocols. This gives them a broad range of applications. For instance, they can analyze speech as it is being spoken, which is especially useful in situations where background noise and voice interference are present.

Deepgram models pick up things like microphone noise profiles, background noise, and transmission protocols

Deepgram models are built from the ground up to process speech and other speech-related data. They can handle thousands of simultaneous audio streams and pick up things like microphone noise profiles, background sounds, and transmission protocols. They can also detect accents, valence, sentiment, and topics of conversation. These speech-recognition systems can be deployed on-premises or in the cloud, and are able to process speech as it is spoken.

The latest demo from Deepgram uses a cloud-based service powered by Amazon Web Services and a GPU. The team processed 212 audio files in 40 seconds. The GPU-based processing power of Deepgram means that it can handle multiple real-time streams at once. The company claims that it can process 300 streams per second using a single GPU.

Deepgram’s software has high accuracy rates and low cost. It can recognize different accents and dialects, and transcribe audio in 16 languages. It also comes with free software tools and an open-source SDK. If you need speech recognition, Deepgram is a great solution. Its API is intuitive and user-friendly, and it supports a variety of languages.

Real-time streaming capability lets customers analyze speech as it’s being spoken

Real-time speech analytics can help businesses improve customer service and reduce costs. It can also help agents give accurate answers to customers’ queries and supervisors train new hires. Businesses are increasingly concerned about customer churn, so good customer experiences are essential for long-term loyalty.

The technology has numerous applications, including closed captioning and live captioning. It can also transcribe pre-recorded media. The input audio is sent through a live streaming protocol, and the output is transmitted through a websocket. The speech understanding feature is then added to the output stream.

Real-time streaming transcription helps businesses understand how customers react to certain sales pitches. They can use real-time transcription to analyze sales pitches and recommend closing tactics. It can also be used for analyzing compliance issues. It can even be used to monitor inappropriate language in video games.

Models can be used in a wide range of applications

The Deepgram series is designed to capture speech, enabling customers to search for words based on their sound. Deepgram models are trained from the ground up to capture speech and store it in a “deep representation index.” The model organizes sounds according to phonetics, enabling customers to search for words using the sound alone. Deepgram models are also capable of detecting misspelled words.

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