Deepgram
Deepgram

WHY DEEPGRAM

Enterprise audio is complex.
Your ASR doesn’t have to be.

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The Problem With Traditional STT

Years ago, speech-to-text was hailed as the big game-changer. It’s great for voice assistants with small sound bites, but for phone calls and meetings it’s slow, inaccurate, expensive, and takes way too long to integrate.

Unique Sounds + Phrases

Every person you do business with has unique vocal attributes, accents, and phrases they like to use. Businesses also have unique product names, technical instructions, and industry terminology hard to recognize.

Why Deepgram?

Because we’re the only platform that learns based on the phonetic patterns of your callers. In addition, our training capabilities allow you to teach your model and improve accuracy overtime.

Why Deepgram?

Because we’re the only platform that learns based on the phonetic patterns of your callers. In addition, our training capabilities allow you to teach your model and improve accuracy overtime.

Poor Audio Quality

Audio from phone or collaboration apps aren’t professionally recorded and have varying compression rates and acoustic environments.

Why Deepgram?

Because we’re the only ASR with a Deep Neural Network that automatically adjusts to microphone noise profiles, audio encodings, and transmission protocols to give you the best possible transcript.

Why Deepgram?

Because we’re the only ASR with a Deep Neural Network that automatically adjusts to microphone noise profiles, audio encodings, and transmission protocols to give you the best possible transcript.

Background Noise

Meeting attendees join from their cars with the radio playing, from echoey conference rooms, and kitchens with children running around. This acoustic environment presents serious issues for traditional STT.

Why Deepgram?

Because we are the only ASR that automatically adjusts to your audio profile, and can distinguish between speakers by the sound of their voices to give you the best possible transcript.

Why Deepgram?

Because we are the only ASR that automatically adjusts to your audio profile, and can distinguish between speakers by the sound of their voices to give you the best possible transcript.

Length of Call

In contrast to consumer use cases, most business audio is far longer than a few seconds. The longer the audio, the harder it is to maintain useful accuracy and return text in a short amount of time.

Why Deepgram?

Because we’re optimized for conversational audio. That means mass quantities of transcription, with understanding. It also means you don’t get charged in 15 second increments if you only used four. Well hello, Scalability.

Why Deepgram?

Because we’re optimized for conversational audio. That means mass quantities of transcription, with understanding. It also means you don’t get charged in 15 second increments if you only used four. Well hello, Scalability.

Unique Sounds + Phrases

Every person you do business with has unique vocal attributes, accents, and phrases they like to use. Businesses also have unique product names, technical instructions, and industry terminology hard to recognize.

Why Deepgram?

Because we’re the only platform that learns based on the phonetic patterns of your callers. In addition, our training capabilities allow you to teach your model and improve accuracy overtime.

Poor Audio Quality

Audio from phone or collaboration apps aren’t professionally recorded and have varying compression rates and acoustic environments.

Why Deepgram?

Because we’re the only ASR with a Deep Neural Network that automatically adjusts to microphone noise profiles, audio encodings, and transmission protocols to give you the best possible transcript.

Background Noise

Meeting attendees join from their cars with the radio playing, from echoey conference rooms, and kitchens with children running around. This acoustic environment presents serious issues for traditional STT.

Why Deepgram?

Because we are the only ASR that automatically adjusts to your audio profile, and can distinguish between speakers by the sound of their voices to give you the best possible transcript.

Length of Call

In contrast to consumer use cases, most business audio is far longer than a few seconds. The longer the audio, the harder it is to maintain useful accuracy and return text in a short amount of time.

Why Deepgram?

Because we’re optimized for conversational audio. That means mass quantities of transcription, with understanding. It also means you don’t get charged in 15 second increments if you only used four. Well hello, Scalability.

Unique Sounds + Phrases

Poor Audio Quality

Background Noise

Length of Call

Human accuracy hype has misguided expectations.

Businesses have tested numerous speech recognition systems under false promises of increased productivity and accuracy, only to find that low accuracy tools create additional problems. Two transcripts can be 85% accurate, or have a word error rate (WER) of 15%, while having vastly different levels of usefulness. 90% accuracy means nothing if the words you care about analyzing or automating actions from are in the missed 10%.

WHY DEEPGRAM?

Because we’re the only 100% deep learning platform that learns from your audio. See upwards of 90% transcription accuracy when you use model training.

WHY DEEPGRAM?

Because we’re the only 100% deep learning platform that learns from your audio. See upwards of 90% transcription accuracy when you use model training.

Cost Prohibitive Solutions = Low Visibility

Due to legacy architectures that are computationally intensive, it was too expensive for enterprises to transcribe their calls. Until now, only 1-30% of customer service calls were transcribed, strictly due to the cost of STT solutions. To make matters worse, if the STT had low accuracy, the customer required a human to validate the data before it can be used.

Why Deepgram?

Because we’re optimized for mass quantities of conversational audio which means you can now process all of your calls. Yes, all of them. Our hardware and usage costs are a hell of a lot lower, too so scalability is a thing you can actually have now.

Why Deepgram?

Because we’re optimized for mass quantities of conversational audio which means you can now process all of your calls. Yes, all of them. Our hardware and usage costs are a hell of a lot lower, too so scalability is a thing you can actually have now.

 

Traditional STT APIs are extremely frustrating to work with.

API requests from leading ASR providers often fail as these systems break with audio longer than 15 seconds, and backups in the queue. This is an overwhelmingly frustrating experience for product, data science and engineering teams.

Why Deepgram?

Because we’re the only 100% deep learning platform that learns from your audio. See upwards of 90% transcription accuracy when you use model training.

Why Deepgram?

Because we’re the only 100% deep learning platform that learns from your audio. See upwards of 90% transcription accuracy when you use model training.

Signs You Need a Stronger ASR Foundation

Engineering
Push-Back

Product leaders have avoided building voice products altogether or try to build in-house solutions. These projects often drag on for 1-3 years with little gained.

High Operations Costs

Call centers are struggling to keep agents productive as calls exponentially increase. Instead of using accurate ASR they’re manually monitoring 1-2% of calls at a high cost.

Lack of BUSINESS Insights

Data Scientists grow frustrated with unparsable speech data. Extra deployment work, slows down projects and in turn the business, as they are left without valuable insights.

Failed CX Projects

Managers are disappointed with failed Customer Experience (CX) initiatives. They worry they’ll fall behind competitors who have made AI or Voice capabilities their differentiator.

Don’t compromise.

Deepgram includes the enterprise features, scalability, and flexibility that businesses need. We’ve reinvented speech recognition with complete, deep learning models that allow you to get more accurate, more reliable transcription, with understanding. We do it better, faster and cheaper than the big guys — on-prem, or in the cloud.

Compare the big ASR’s to Deepgram

Model Training

Each model is tuned to the audio you care about. This is done through state-of-the- art data labeling and model training.

Custom Vocabulary

Every expert model is trained to the phonetic patterns of your callers or meeting attendees. You can submit keywords parameters as a way to give the model a little context for what it’s hearing in a submitted audio file.

Language Support

Accurately identify and transcribe audio across multiple languages, accents and dialects.

Multi-Channel Support

Reliably identify speaker changes across single and multi-channel audio.

Timestamps

Each word includes an associated timestamp. Drill into audio snippets with specific start and end times.

Punctuation

Use punctuation in your transcripts to make them easier for humans, and machines to read.

Realtime Streaming

Keep the conversation flowing. Transcribe phone and meeting conversations as they happen.

Deep Search

Find specific terms or phrases within transcripts based on phonetic patterns, not text.

Redaction

Automatically redact sensitive data such as PCI from transcripts.

Diarization

Identify up to 10 different speakers at one time. Don’t worry we won’t charge you multiple times.

On-Premise Deploy

Train your models and deploy anywhere – on premises, VPC or in the cloud.

Dedicated VPC Deploy
API

Connect to any audio data source and deliver accurate transcripts to the user facing system of your choice.

Speech-to-Text

Accurately convert Enterprise audio to text at scale with an easy-to-use API or through our dashboard.

GOOGLE
AMAZON
NUANCE
IBM
DEEPGRAM
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GOOGLE
AMAZON
NUANCE
IBM
DEEPGRAM
 
GOOGLE
AMAZON
NUANCE
IBM
DEEPGRAM
Model Training

Each model is tuned to the audio you care about. This is done through state-of-the- art data labeling and model training.

None
None
None
None
Great
Custom Vocabulary

Every expert model is trained to the phonetic patterns of your callers or meeting attendees. You can submit keywords parameters as a way to give the model a little context for what it’s hearing in a submitted audio file.

Poor
Poor
Poor
Poor
Great
Language Support

Accurately identify and transcribe audio across multiple languages, accents and dialects.

Great
Average
Average
Average
Great
Multi-Channel Support

Reliably identify speaker changes across single and multi-channel audio.

None
None
Average
Average
Great
Timestamps

Each word includes an associated timestamp. Drill into audio snippets with specific start and end times.

Average
Average
Average
Average
Great
Punctuation

Use punctuation in your transcripts to make them easier for humans, and machines to read.

Poor
Average
Average
Average
Great
Realtime Streaming

Keep the conversation flowing. Transcribe phone and meeting conversations as they happen.

Poor
None
Average
Average
Great
Deep Search

Find specific terms or phrases within transcripts based on phonetic patterns, not text.

Poor
Poor
Poor
Poor
Great
Redaction

Automatically redact sensitive data such as PCI from transcripts.

Average
Average
Average
Average
Great
Diarization

Identify up to 10 different speakers at one time. Don’t worry we won’t charge you multiple times.

Poor
Average
Average
Average
Great
On-Premise Deploy

Train your models and deploy anywhere – on premises, VPC or in the cloud.

Poor
Average
Average
Average
Great
Dedicated VPC Deploy
None
None
Poor
Poor
Great
API

Connect to any audio data source and deliver accurate transcripts to the user facing system of your choice.

Average
Average
Average
Average
Great
Speech-to-Text

Accurately convert Enterprise audio to text at scale with an easy-to-use API or through our dashboard.

Average
Average
Poor
Poor
Great

It’s your data. Do something BIG with it.

Contact Us