Editing Speech recognition (section)

====2010s====
By early 2010s ''speech'' recognition, also called voice recognition<ref>{{Cite web |date=27 August 2002 |title=Improvements in voice recognition software increase |url=https://www.techrepublic.com/article/improvements-in-voice-recognition-software-increase-productivity |url-status=dead |archive-url=https://web.archive.org/web/20181023080207/https://www.techrepublic.com/article/improvements-in-voice-recognition-software-increase-productivity/ |archive-date=23 October 2018 |access-date=22 October 2018 |website=TechRepublic.com |quote=Maners said IBM has worked on advancing speech recognition ... or on the floor of a noisy trade show.}}</ref><ref>{{Cite web |date=3 March 1997 |title=Voice Recognition To Ease Travel Bookings: Business Travel News |url=http://www.businesstravelnews.com/More-News/Voice-Recognition-To-Ease-Travel-Bookings |website=BusinessTravelNews.com |quote=The earliest applications of speech recognition software were dictation ... Four months ago, IBM introduced a 'continual dictation product' designed to ... debuted at the National Business Travel Association trade show in 1994. |access-date=9 September 2024 |archive-date=9 September 2024 |archive-url=https://web.archive.org/web/20240909052252/https://www.businesstravelnews.com/More-News/Voice-Recognition-To-Ease-Travel-Bookings |url-status=live }}</ref><ref>{{Cite news |last=Ellis Booker |date=14 March 1994 |title=Voice recognition enters the mainstream |work=[[Computerworld]] |page=45 |quote=Just a few years ago, speech recognition was limited to ...}}</ref> was clearly differentiated from ''speaker'' recognition, and speaker independence was considered a major breakthrough. Until then, systems required a "training" period.  A 1987 ad for a doll had carried the tagline "Finally, the doll that understands you." – despite the fact that it was described as "which children could train to respond to their voice".<ref name=PCW.Siri/>

In 2017, Microsoft researchers reached a historical human parity milestone of transcribing conversational telephony speech on the widely benchmarked Switchboard task. Multiple deep learning models were used to optimize speech recognition accuracy. The speech recognition word error rate was reported to be as low as 4 professional human transcribers working together on the same benchmark, which was funded by IBM Watson speech team on the same task.<ref>{{Cite web |date=21 August 2017 |title=Microsoft researchers achieve new conversational speech recognition milestone |url=https://www.microsoft.com/en-us/research/blog/microsoft-researchers-achieve-new-conversational-speech-recognition-milestone/ |website=[[Microsoft]] |access-date=9 September 2024 |archive-date=9 September 2024 |archive-url=https://web.archive.org/web/20240909052234/https://www.microsoft.com/en-us/research/blog/microsoft-researchers-achieve-new-conversational-speech-recognition-milestone/ |url-status=live }}</ref>