Many AWS clients have been successfully using Amazon Transcribe to accurately, efficiently, and automatically convert their client audio conversations to text, and extract actionable insights from them. These insights can help you continuously improve the processes and products that directly enhance the quality and experience for your customers.

Get insights from Customer Conversations

With Transcribe Call Analytics, quickly extract actionable insights from customer conversations. Contact Center Intelligence partners and Contact Lens for Amazon Connect offer turnkey solutions to improve customer engagement, increase agent productivity, and surface quality management alerts to supervisors.

Improve Clinical Documentation

Medical doctors and practitioners can use Amazon Transcribe Medical to quickly and efficiently document clinical conversations into electronic health record (EHR) systems for analysis. The service is HIPAA- eligible and trained to understand medical terminology.

Domain Specific Models

Choose a model tuned to telephone calls or multimedia video content. For example, Transcribe adapts to low-fidelity phone audio standards in contact centers.

Automatic language identification

Automatic Language Identification

With Amazon Transcribe, you can automatically specify the dominant language in an audio file and generate transcriptions. This is helpful when your media library contains audio files in various languages. You can also use this feature for media content classification and verify that the primary spoken language in your videos and podcasts is accurately labeled.

Custom Vocabulary

With custom phrasing, you can add new words to the base vocabulary to develop more accurate transcriptions for domain-specific words and phrases like product names, technical terminology, or names of people.

Custom Language Models

When needed, you can create and train your own custom language model (CLM) for your use case and domain by offering a corpus of text data to Amazon Transcribe. CLM is a convenient feature for improving speech recognition accuracy with your own data.

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Success Stories

Over the past decade, The New York Times has shifted from a traditional ad-based print business model to a subscriber-first digital model, evolving the most successful digital subscription news community in the world. See how The New York Times operates Contact Lens for Amazon Connect (which is built on Amazon Transcribe and Amazon Comprehend) to help provide its Customer Care team meet the demands of millions of new digital subscribers.

Reinventing Hybrid Cooperation with Machine Learning

Seeing a possibility to reinvent AI-driven ways to connect communities online, Slack partnered with AWS to recreate in-person collaboration touchpoints on the cloud. Powered by AWS’s powerful data and ML services (Amazon Transcribe and Amazon Chime SDK), Slack engineers have developed tools that let us hop in and out of standups, collaborate in conference rooms, and even casually chat with colleagues face-to-face.

Extracting insights from customer discussions

Helping home shoppers connect to the services they need in time can make the difference if they are successful in ensuring a property or not. In this episode, we examine how Zillow built a natural language processing solution utilizing Amazon Transcribe and the Elastic Container Service to quickly scale their machine learning engine to fit customer requests to agents.

Helping members work their health

There are about 150 million people living with a chronic condition in the U.S. Chronic conditions account for approximately 90% of the current spending in the U.S. Healthcare system. Learn how Livongo, part of Teladoc Health — a global leader in virtual care, uses Amazon Transcribe and AWS Machine Learning to allow Americans to manage their chronic conditions from a mobile application.

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