By ET Bureau - July 23, 2019 2 Mins Read
Rev.com launched Rev Automated Transcription, a service that creates affordable, easy-to-follow transcripts that power productivity. The new service complements the company’s established leadership in human-powered transcription and adds functionality to Rev’s convenient platform. The automatic speech recognition (ASR) engine powering the service transcribes speech-to-text in minutes and exceeds 90 percent accuracy on files with clear audio, beating Google, Amazon, and Microsoft in tests.
“We realize there are times when businesses and individuals may prefer an automated transcript over a human transcript. We’re thrilled to better serve customers by offering both options in one convenient platform,” said Jason Chicola, Co-founder and CEO of Rev.com. “Our scientists have built one of the best speech recognition engines on the market, empowering users to store, search and capture insights from the spoken word. We hope to become an integral part of people’s lives while liberating users to focus on their skills and let Rev drive efficiencies for them.”
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Established in 2010, Rev has become a trusted provider for fast, accurate, and affordable audio and video-to-text needs. Unlike others in the voice market, Rev built its ASR engine from the ground up, training it on more than fifty thousand hours of accurately transcribed audio samples. Results from a recent Word Error Rates (WER) test on a collection of public podcasts illustrate the accuracy of its ASR engine. Rev’s service yielded the lowest WER compared to major providers in this space.
In addition to excelling on word accuracy tests, Rev Automated Transcription captures highly accurate punctuation and can distinguish between speakers, creating easy-to-follow transcripts. Rev achieved industry-leading results by developing software that leverages large quantities of accurately transcribed audio data and applying advanced deep learning methods. Rev also uses Natural Language Processing (NLP) techniques to derive meaning from the content of colloquialisms, abbreviations, and acronyms, producing more accurate results without a human.
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