Equature released its automated transcription service built directly into their recording software. Equature's transcription automatically transcribes all audio captured by its recording system and aligns it with the call to easily listen and follow along throughout the transcription. Equature Transcription is a first-of-its-kind transcription and full-text search engine. Equature Transcription provides automated transcription of audio from 9-1-1 calls, radio transmissions, Equature Armor Body-worn Camera video, and any other form of media captured within the Equature recording system. Once transcribed, all written text is searchable within the system.

Creating content has become increasingly important for businesses of all kinds, and this is especially so with the ever-growing need for more. Consumers want more than just a product description or an About Us section on your website: They want to engage and feel like they are part of something. That means you and your company are going to have to deliver. For instance, you might have a podcast or video series that you've created to help customers understand how a product works. Once you've created those video and/or audio files, you upload them to your site and share them with social media.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Last week, OpenAI released Whisper, an open-source deep learning model for speech recognition. Developers and researchers who have experimented with Whisper are also impressed with what the model can do. However, what is perhaps equally important is what Whisper's release tells us about the shifting culture in artificial intelligence (AI) research and the kind of applications we can expect in the future.

AI-powered transcription service Otter.ai is today announcing a complete overhaul of its platform, offering workers new intelligently generated in-meeting action items, alongside a centralized environment for all meeting transcriptions and notes. Building on the launch of its Otter Assistant in August 2021, the new update aims to streamline communication further by continuing to use conversational AI to improve both the in-meeting and post-meeting experience. The update consists of a new-look home feed, which now acts as a centralized hub for all meeting and post-meeting actions. Users can connect their Google or Microsoft Outlook calendars to Otter to keep track of upcoming meetings, use it to directly join meetings or schedule their Otter Assistant to join. Shared conversations, highlights and comments, and tagged action items will also all be accessible from the new home feed.

It's no secret that voice recognition has advanced significantly since IBM introduced its first speech recognition machine in 1962. With voice-driven applications like Amazon's Alexa, Apple's Siri, Microsoft's Cortana, and many voice-responsive features of Google, voice recognition has become increasingly embedded in our daily lives as technology has evolved. Every new voice-interactive device we introduce into our lives, from phones to computers to watches to refrigerators, increases our reliance on artificial intelligence (AI) and machine learning. Artificial intelligence is one disruptive technology that has altered the way valuable data is handled. When working with large analyzable sets of data, such as text, machine learning is thought to be at its best.

This post provides steps and python syntax for utilizing the Google Cloud Platform speech transcription service. Speech transcription refers to the conversion of speech audio to text. This can be applied to many use cases such as voice assistants, dictation, customer service call center documentation, or creation of meeting notes in an office business setting. It is not difficult to see the value this can bring to individuals and businesses. AWS has long been a leader in this space. Google, IBM, and Microsoft have of course developed their own services as well.

Microsoft's recent shopping spree reached a new climax this week with the announcement of its $19.7 billion acquisition of Nuance, a company that provides speech recognition and conversational AI services. Nuance is best known for its deep learning voice transcription service, which is very popular in the health care sector. The two companies had already been working together closely before the acquisition. Nuance had built several of its products on top of Microsoft's Azure cloud. And Microsoft had been using Nuance's Dragon service in its Cloud for Healthcare solution, which launched last year in the midst of the pandemic.

I've searched for a reliable way to autonomously transcribe natural speech for years. I'm a journalist, and I often have hours of taped interviews with sources around the globe to transcribe. Speech to text has been a huge challenge for AI developers, and it's a puzzle that's being closely watched in a variety of industries. The technology has implications far beyond quoting sources; human-machine interfaces in fields like robotics, autonomous vehicles, and personal computing will benefit from computers that can accurately interpret natural speech. Transcription, then, is a kind of technological entry point, a straightforward market need that can help spur development of a technology that will have broad resonance and incalculable implications for how we interact with machines.

AI is one of the driving forces behind what The World Economic Forum called "The Fourth Industrial Revolution". Developments in this area are expected to help us further automate our workflows and simplify our daily tasks, making everything from our food production chains to management and even medical procedures, far more effective and agile. And, according to PwC, AI is expected to add up to 15.6 trillion dollars to the world economy by 2030. AI is getting smarter faster than ever, with established players, such as Google or Amazon developing and integrating AI into their products and operations, and a generation of startups from all around the globe, developing and offering AI-based tools. One of the main areas that AI is starting to be used in, is transcription services.

Amazon Web Services is rolling out an electronic health record-supported machine learning transcription service that uses speech recognition applications to ease physician documentation. The product is Amazon Transcribe Medical, which automatically translates audio streams into medical speech, enabling affordable, secure and accurate note taking for clinical staff, researchers and other stakeholders. Cerner, for example is using the product in an initial development of a digital voice scribe that automatically listens to clinician and patient interactions and captures the conversation in text form. The service enables developers to add medical speech-to-text capability to their applications. Amazon is positioning Transcribe Medical as a tool to ease physician and researcher burnout.