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.

Google is restarting a practice in which human contractors listen to and transcribe some voice commands people give to the company's artificial intelligence system, Assistant. But this time Google is taking steps to make sure people know what they are agreeing to. The company suspended its transcription practices after more than 1,000 Dutch-language recordings were leaked to the media in Belgium this summer. Google required users to opt-in to the service before audio transcriptions were recorded, but critics have said people didn't fully understand they were agreeing to allow human transcribers to listen in because the company's language was unclear. Amazon, Microsoft, Apple and Facebook have all used similar practices.

Microsoft announced today that its conversational speech recognition system has reached a 5.1% error rate, its lowest so far. This surpasses the 5.9% error rate reached last year by a group of researchers from Microsoft Artificial Intelligence and Research and puts its accuracy on par with professional human transcribers who have advantages like the ability to listen to text several times. Both studies transcribed recordings from the Switchboard corpus, a collection of about 2,400 telephone conversations that have been used by researchers to test speech recognition systems since the early 1990s. The new study was performed by a group of researchers at Microsoft AI and Research with the goal of achieving the same level of accuracy as a group of human transcribers who were able to listen to what they were transcribing several times, access its conversational context and work with other transcribers. Overall, researchers from the latest study reduced the error rate by about 12 percent compared to last year's findings by improving the neural net-based acoustic and language models of Microsoft's speech recognition system.

Microsoft recently announced that its conversational system for speech recognition has achieved a 5.1 percent error rate, its best performance to date. This beats the 5.9 percent error rate achieved in October of 2016 and put its accuracy at the same level as professional human transcribers, who can listen to text multiple times, access cultural context, and collaborate with other transcribers. After the 2016 study, other researchers set the human parity rate at a 5.1 percent error rate. Therefore, even using the more conservative standard, the system has achieved human parity. The recordings that formed the basis of both studies came from the Switchboard collection, a research collection of thousands of telephone conversations used to test speech recognition systems since the early 1990s.

We describe a method for continuously improving the accuracy of a large-scale medical automatic speech recognizer (ASR) using a multi-step cycle involving several groups of workers. The paper will address the unique challenges of the medical domain, and discuss how automatically created and crowdsourced input data is combined to refine the ASR language models. The improvement cycle helped to decrease the original system's word error rate from 34.1% to 10.4%, which approaches the accuracy of human transcribers trained in medical transcription.

Moore's law has driven silicon chip circuitry to the point where we are surrounded by devices equipped with microprocessors. The devices are frequently wonderful; communicating with them – not so much. Pressing buttons on smart devices or keyboards is often clumsy and never the method of choice when effective voice communication is possible. The keyword in the previous sentence is "effective". Technology has advanced to the point where we are in the early stages of being able to communicate with our devices using voice recognition.