By AI Trends Staff
Advances in the AI behind speech recognition are driving growth in the market, attracting venture capital and funding startups, posing challenges to established players.
The growing acceptance and use of speech recognition devices are driving the market, which according to an estimate by Meticulous Research is expected to reach $26.8 billion globally by 2025, according to a recent account in Analytics Insight. Better speed and accuracy are among the benefits of the evolving technology.
One company in the throes of this new growth, AssemblyAI of San Francisco, is offering an API for speech recognition capable of transcribing videos, podcasts, phone calls, and remote meetings. The company was founded by CEO Dylan Fox in 2017 and has received backing from Y Combinator, a startup accelerator, as well as NVIDIA.
Fox has an unusual background for a high tech entrepreneur. He is a graduate of George Washington University with a degree in business administration, business economics, and public policy. He got a job as a software engineer for machine learning in the emerging product lab of Cisco in San Francisco, working on deep neural networks and machine learning. He got the idea for AssemblyAi and attracted capital from Y Combinator, which enabled him to hire data scientists and data engineers to get the technology off the ground.
Asked in an interview with AI Trends how he made this transition from undergrad in business administration and economics to high-tech entrepreneur, Fox said, “I taught myself how to program, which led me to a path of machine learning. I was looking for a harder software challenge, which led to natural language processing, which took me to Cisco.” They were working on Siri for the Enterprise for Apple at the time,
To speed up the work, Cisco was looking to acquire speech recognition software; Fox was in the catbird’s seat for the search. “We looked at Nuance,” for example, acknowledged as a market leader and owner of more speech recognition software than its competitors. (The acquisition of Nuance by Microsoft for $19.6 billion is expected to be finalized by year-end.) The young, budding entrepreneur was not impressed. “It was crazy how bad all the options were from an accuracy and a developer point of view,” he stated.
He was impressed by Twilio, a San Francisco-based company founded in 2008, which that year released the Twilio Voice API to make and receive phone calls hosted in the cloud. The company has since raised $103 million in venture capital. “They were setting new standards for a good API for developers,” Fox said.
Fox’s idea was to use AI and machine learning to achieve “super accurate results, and make it easy for developers to incorporate the API into their products. One customer is CallRail, offering call tracking and marketing analytics software, which plans to incorporate AssembyAI’s API to gain insight into why people are calling. Other customers include NBC and the Wall Street Journal, using the product to transcribe content and interviews, and provide closed captioning.
“We’ve been working on building as close to human speech recognition quality as possible. It’s been a lot of work” Fox said. He expects to reach that plateau in 2022.
He targets companies incorporating speech recognition into their products and makes it easy to buy. Customers pay on a usage basis; for every second of audio transcribed, AssemblyAI charges a fraction of a penny. Clients get billed monthly. If a customer uses 10 hours a month, it costs about nine dollars. If a customer uses a million hours a month, it costs about $900,000.
Voice recognition is a hot market. “Many new startups are being launched,” Fox said, providing opportunity. “Many interesting new businesses are being built on voice data.”
AssemblyAI’s product can detect sensitive topics such as hate speech and profanity, so customers can save on human content moderation.
Asked to describe what differentiates his technology, Fox said, “We are an experienced team of deep learning researchers,” with experience from companies including BMW, Apple, and Facebook. “We build very large, very accurate deep learning models that have recognition results far more accurate than a traditional machine learning approach. We build really large models using advanced neural network technologies.” He compared the approach to what OpenAI uses to develop its GPT-3 large language model.
In addition, they build AI features on top of the transcriptions, to provide summaries of audio and video content, which can be searched and indexed. “It goes beyond just transcription,” Fox said.
The company currently has 25 employees and expects to double in about four months. Business has been good. “There is an explosion of audio and video data online and customers want to be able to take advantage of it, so we see a lot of demand,” Fox said.
Learn more at AssemblyAI.