Editing Speech recognition (section)

==Applications==

===In-car systems===
Typically a manual control input, for example by means of a finger control on the steering-wheel, enables the speech recognition system and this is signaled to the driver by an audio prompt. Following the audio prompt, the system has a "listening window" during which it may accept a speech input for recognition. {{citation needed|date=March 2014}}
 
Simple voice commands may be used to initiate phone calls, select radio stations or play music from a compatible smartphone, MP3 player or music-loaded flash drive. Voice recognition capabilities vary between car make and model. Some of the most recent{{When|date=April 2014}} car models offer natural-language speech recognition in place of a fixed set of commands, allowing the driver to use full sentences and common phrases. With such systems there is, therefore, no need for the user to memorize a set of fixed command words.{{citation needed|date=March 2014}}

===Education===
{{main|Pronunciation assessment}}

Automatic [[pronunciation]] assessment is the use of speech recognition to verify the correctness of pronounced speech,<ref>{{Citation |last1=El Kheir |first1=Yassine |title=Automatic Pronunciation Assessment — A Review |date=October 21, 2023 |publisher=Conference on Empirical Methods in Natural Language Processing |arxiv=2310.13974 |s2cid=264426545 |display-authors=1 |last2=Ali |first2=Ahmed}}</ref> as distinguished from manual assessment by an instructor or proctor.<ref>{{Cite journal |last1=Isaacs |first1=Talia |last2=Harding |first2=Luke |date=July 2017 |title=Pronunciation assessment |journal=Language Teaching |language=en |volume=50 |issue=3 |pages=347–366 |doi=10.1017/S0261444817000118 |issn=0261-4448 |s2cid=209353525 |doi-access=free}}</ref> Also called speech verification, pronunciation evaluation, and pronunciation scoring, the main application of this technology is computer-aided pronunciation teaching (CAPT) when combined with [[computer-aided instruction]] for [[computer-assisted language learning]] (CALL), speech [[Remedial education|remediation]], or [[accent reduction]]. Pronunciation assessment does not determine unknown speech (as in [[Digital dictation|dictation]] or [[automatic transcription]]) but instead, knowing the expected word(s) in advance, it attempts to verify the correctness of the learner's pronunciation and ideally their [[Intelligibility (communication)|intelligibility]] to listeners,<ref>{{Citation |last1=Loukina |first1=Anastassia |title=INTERSPEECH 2015 |date=September 6, 2015 |pages=1917–1921 |chapter=Pronunciation accuracy and intelligibility of non-native speech |chapter-url=https://www.isca-speech.org/archive/pdfs/interspeech_2015/loukina15_interspeech.pdf |place=Dresden, Germany |publisher=[[International Speech Communication Association]] |quote=only 16% of the variability in word-level intelligibility can be explained by the presence of obvious mispronunciations. |display-authors=1 |last2=Lopez |first2=Melissa |access-date=9 September 2024 |archive-date=9 September 2024 |archive-url=https://web.archive.org/web/20240909053932/https://www.isca-speech.org/archive/pdfs/interspeech_2015/loukina15_interspeech.pdf |url-status=live }}</ref><ref name="obrien">{{Cite journal |last1=O’Brien |first1=Mary Grantham |last2=Derwing |first2=Tracey M. |display-authors=1 |date=31 December 2018 |title=Directions for the future of technology in pronunciation research and teaching |journal=Journal of Second Language Pronunciation |language=en |volume=4 |issue=2 |pages=182–207 |doi=10.1075/jslp.17001.obr |issn=2215-1931 |s2cid=86440885 |quote=pronunciation researchers are primarily interested in improving L2 learners’ intelligibility and comprehensibility, but they have not yet collected sufficient amounts of representative and reliable data (speech recordings with corresponding annotations and judgments) indicating which errors affect these speech dimensions and which do not. These data are essential to train ASR algorithms to assess L2 learners’ intelligibility. |doi-access=free |hdl-access=free |hdl=2066/199273}}</ref> sometimes along with often inconsequential [[Prosody (linguistics)|prosody]] such as [[Intonation (linguistics)|intonation]], [[Pitch (music)|pitch]], [[Speech tempo|tempo]], [[Isochrony|rhythm]], and [[Vocal stress|stress]].<ref>{{Cite journal |last=Eskenazi |first=Maxine |date=January 1999 |title=Using automatic speech processing for foreign language pronunciation tutoring: Some issues and a prototype |url=https://www.lltjournal.org/item/10125-25043/ |journal=Language Learning & Technology |language=en |volume=2 |issue=2 |pages=62–76 |access-date=11 February 2023 |archive-date=9 September 2024 |archive-url=https://web.archive.org/web/20240909053942/https://www.lltjournal.org/item/10125-25043/ |url-status=live }}</ref> Pronunciation assessment is also used in [[reading tutoring]], for example in products such as [[Microsoft Teams]]<ref>{{Cite news |last=Tholfsen |first=Mike |date=9 February 2023 |title=Reading Coach in Immersive Reader plus new features coming to Reading Progress in Microsoft Teams |url=https://techcommunity.microsoft.com/t5/education-blog/reading-coach-in-immersive-reader-plus-new-features-coming-to/ba-p/3734079 |access-date=12 February 2023 |work=Techcommunity Education Blog |publisher=Microsoft |language=en |archive-date=9 September 2024 |archive-url=https://web.archive.org/web/20240909052822/https://techcommunity.microsoft.com/t5/education-blog/reading-coach-in-immersive-reader-plus-new-features-coming-to/ba-p/3734079 |url-status=live }}</ref> and from Amira Learning.<ref>{{Cite news |last=Banerji |first=Olina |date=7 March 2023 |title=Schools Are Using Voice Technology to Teach Reading. Is It Helping? |url=https://www.edsurge.com/news/2023-03-07-schools-are-using-voice-technology-to-teach-reading-is-it-helping |access-date=7 March 2023 |work=EdSurge News |language=en |archive-date=9 September 2024 |archive-url=https://web.archive.org/web/20240909054611/https://www.edsurge.com/news/2023-03-07-schools-are-using-voice-technology-to-teach-reading-is-it-helping |url-status=live }}</ref> Automatic pronunciation assessment can also be used to help diagnose and treat [[speech disorders]] such as [[speech apraxia|apraxia]].<ref>{{Cite book |last1=Hair |first1=Adam |url=https://psi.engr.tamu.edu/wp-content/uploads/2018/04/hair2018idc.pdf |title=Proceedings of the 17th ACM Conference on Interaction Design and Children |last2=Monroe |first2=Penelope |date=19 June 2018 |isbn=9781450351522 |pages=119–131 |chapter=Apraxia world: A speech therapy game for children with speech sound disorders |doi=10.1145/3202185.3202733 |display-authors=1 |s2cid=13790002 |access-date=9 September 2024 |archive-date=9 September 2024 |archive-url=https://web.archive.org/web/20240909052803/https://psi.engr.tamu.edu/wp-content/uploads/2018/04/hair2018idc.pdf |url-status=live }}</ref>

Assessing authentic listener intelligibility is essential for avoiding inaccuracies from [[Accent (sociolinguistics)|accent]] bias, especially in high-stakes assessments;<ref>{{Cite news |date=8 August 2017 |title=Computer says no: Irish vet fails oral English test needed to stay in Australia |url=https://www.theguardian.com/australia-news/2017/aug/08/computer-says-no-irish-vet-fails-oral-english-test-needed-to-stay-in-australia |access-date=12 February 2023 |work=The Guardian |agency=Australian Associated Press |archive-date=9 September 2024 |archive-url=https://web.archive.org/web/20240909052806/https://www.theguardian.com/australia-news/2017/aug/08/computer-says-no-irish-vet-fails-oral-english-test-needed-to-stay-in-australia |url-status=live }}</ref><ref>{{Cite news |last=Ferrier |first=Tracey |date=9 August 2017 |title=Australian ex-news reader with English degree fails robot's English test |url=https://www.smh.com.au/technology/australian-exnews-reader-with-english-degree-fails-robots-english-test-20170809-gxsjv2.html |access-date=12 February 2023 |work=The Sydney Morning Herald |language=en |archive-date=9 September 2024 |archive-url=https://web.archive.org/web/20240909053307/https://www.smh.com.au/technology/australian-exnews-reader-with-english-degree-fails-robots-english-test-20170809-gxsjv2.html |url-status=live }}</ref><ref>{{Cite news |last1=Main |first1=Ed |last2=Watson |first2=Richard |date=9 February 2022 |title=The English test that ruined thousands of lives |url=https://www.bbc.com/news/uk-60264106 |access-date=12 February 2023 |work=BBC News |archive-date=9 September 2024 |archive-url=https://web.archive.org/web/20240909054614/https://www.bbc.com/news/uk-60264106 |url-status=live }}</ref> from words with multiple correct pronunciations;<ref>{{Cite web |last=Joyce |first=Katy Spratte |date=January 24, 2023 |title=13 Words That Can Be Pronounced Two Ways |url=https://www.rd.com/list/words-that-can-be-pronounced-two-ways/ |access-date=23 February 2023 |publisher=Reader's Digest |archive-date=9 September 2024 |archive-url=https://web.archive.org/web/20240909054447/https://www.rd.com/list/words-that-can-be-pronounced-two-ways/ |url-status=live }}</ref> and from phoneme coding errors in machine-readable pronunciation dictionaries.<ref>E.g., [[CMU Pronouncing Dictionary|CMUDICT]], {{Cite web |title=The CMU Pronouncing Dictionary |url=http://www.speech.cs.cmu.edu/cgi-bin/cmudict |access-date=15 February 2023 |website=www.speech.cs.cmu.edu |archive-date=15 August 2010 |archive-url=https://web.archive.org/web/20100815023012/http://www.speech.cs.cmu.edu/cgi-bin/cmudict |url-status=live }} Compare "four" given as "F AO R" with the vowel AO as in "caught," to "row" given as "R OW" with the vowel OW as in "oat."</ref> In 2022, researchers found that some newer speech to text systems, based on [[end-to-end reinforcement learning]] to map audio signals directly into words, produce word and phrase confidence scores very closely correlated with genuine listener intelligibility.<ref>{{Cite conference |last1=Tu |first1=Zehai |last2=Ma |first2=Ning |last3=Barker |first3=Jon |date=2022 |title=Unsupervised Uncertainty Measures of Automatic Speech Recognition for Non-intrusive Speech Intelligibility Prediction |url=https://www.isca-speech.org/archive/pdfs/interspeech_2022/tu22b_interspeech.pdf |conference=INTERSPEECH 2022 |publisher=ISCA |pages=3493–3497 |doi=10.21437/Interspeech.2022-10408 |access-date=17 December 2023 |book-title=Proc. Interspeech 2022 |archive-date=9 September 2024 |archive-url=https://web.archive.org/web/20240909053824/https://www.isca-speech.org/archive/pdfs/interspeech_2022/tu22b_interspeech.pdf |url-status=live }}</ref> In the [[Common European Framework of Reference for Languages]] (CEFR) assessment criteria for "overall phonological control", intelligibility outweighs formally correct pronunciation at all levels.<ref>{{Cite book |url=https://rm.coe.int/cefr-companion-volume-with-new-descriptors-2018/1680787989 |title=Common European framework of reference for languages learning, teaching, assessment: Companion volume with new descriptors |date=February 2018 |publisher=Language Policy Programme, Education Policy Division, Education Department, [[Council of Europe]] |page=136 |oclc=1090351600 |access-date=9 September 2024 |archive-date=9 September 2024 |archive-url=https://web.archive.org/web/20240909053825/https://rm.coe.int/cefr-companion-volume-with-new-descriptors-2018/1680787989 |url-status=live }}</ref>

===Health care===

====Medical documentation====
In the [[health care]] sector, speech recognition can be implemented in front-end or back-end of the medical documentation process. Front-end speech recognition is where the provider dictates into a speech-recognition engine, the recognized words are displayed as they are spoken, and the dictator is responsible for editing and signing off on the document. Back-end or deferred speech recognition is where the provider dictates into a [[digital dictation]] system, the voice is routed through a speech-recognition machine and the recognized draft document is routed along with the original voice file to the editor, where the draft is edited and report finalized. Deferred speech recognition is widely used in the industry currently.

One of the major issues relating to the use of speech recognition in healthcare is that the [[American Recovery and Reinvestment Act of 2009]] ([[American Recovery and Reinvestment Act of 2009|ARRA]]) provides for substantial financial benefits to physicians who utilize an EMR according to "Meaningful Use" standards. These standards require that a substantial amount of data be maintained by the EMR (now more commonly referred to as an [[Electronic Health Record]] or EHR). The use of speech recognition is more naturally suited to the generation of narrative text, as part of a radiology/pathology interpretation, progress note or discharge summary: the ergonomic gains of using speech recognition to enter structured discrete data (e.g., numeric values or codes from a list or a [[controlled vocabulary]]) are relatively minimal for people who are sighted and who can operate a keyboard and mouse.

A more significant issue is that most EHRs have not been expressly tailored to take advantage of voice-recognition capabilities. A large part of the clinician's interaction with the EHR involves navigation through the user interface using menus, and tab/button clicks, and is heavily dependent on keyboard and mouse: voice-based navigation provides only modest ergonomic benefits. By contrast, many highly customized systems for radiology or pathology dictation implement voice "macros", where the use of certain phrases – e.g., "normal report", will automatically fill in a large number of default values and/or generate boilerplate, which will vary with the type of the exam – e.g., a chest X-ray vs. a gastrointestinal contrast series for a radiology system.

====Therapeutic use====
Prolonged use of speech recognition software in conjunction with [[word processor]]s has shown benefits to short-term-memory restrengthening in [[brain AVM]] patients who have been treated with [[Resection (surgery)|resection]]. Further research needs to be conducted to determine cognitive benefits for individuals whose AVMs have been treated using radiologic techniques.{{citation needed|date=November 2016}}

===Military===

====High-performance fighter aircraft====
Substantial efforts have been devoted in the last decade to the test and evaluation of speech recognition in [[fighter aircraft]]. Of particular note have been the US program in speech recognition for the [[General Dynamics F-16 Fighting Falcon variants#F-16_Advanced_Fighter_Technology_Integration|Advanced Fighter Technology Integration (AFTI)]]/[[F-16]] aircraft ([[F-16 VISTA]]), the program in France for [[Mirage (aircraft)|Mirage]] aircraft, and other programs in the UK dealing with a variety of aircraft platforms. In these programs, speech recognizers have been operated successfully in fighter aircraft, with applications including setting radio frequencies, commanding an autopilot system, setting steer-point coordinates and weapons release parameters, and controlling flight display.

Working with Swedish pilots flying in the [[Saab JAS 39 Gripen|JAS-39]] Gripen cockpit, Englund (2004) found recognition deteriorated with increasing [[g-force|g-loads]]. The report also concluded that adaptation greatly improved the results in all cases and that the introduction of models for breathing was shown to improve recognition scores significantly. Contrary to what might have been expected, no effects of the broken English of the speakers were found. It was evident that spontaneous speech caused problems for the recognizer, as might have been expected. A restricted vocabulary, and above all, a proper syntax, could thus be expected to improve recognition accuracy substantially.<ref>{{Cite thesis |last=Englund |first=Christine |title=Speech recognition in the JAS&nbsp;39 Gripen aircraft: Adaptation to speech at different G-loads |degree=Masters thesis |publisher=[[Stockholm University|Stockholm Royal Institute of Technology]] |url=http://www.speech.kth.se/prod/publications/files/1664.pdf |year=2004 |url-status=live |archive-url=https://web.archive.org/web/20081002002102/http://www.speech.kth.se/prod/publications/files/1664.pdf |archive-date=2 October 2008 |df=dmy-all}}</ref>

The [[Eurofighter Typhoon]], currently in service with the UK [[RAF]], employs a speaker-dependent system, requiring each pilot to create a template. The system is not used for any safety-critical or weapon-critical tasks, such as weapon release or lowering of the undercarriage, but is used for a wide range of other cockpit functions. Voice commands are confirmed by visual and/or aural feedback. The system is seen as a major design feature in the reduction of pilot [[workload]],<ref>{{Cite web |title=The Cockpit |url=https://www.eurofighter.com/the-aircraft#cockpit |url-status=live |archive-url=https://web.archive.org/web/20170301222529/https://www.eurofighter.com/the-aircraft#cockpit |archive-date=1 March 2017 |website=Eurofighter Typhoon |df=dmy-all}}</ref> and even allows the pilot to assign targets to his aircraft with two simple voice commands or to any of his wingmen with only five commands.<ref>{{Cite web |title=Eurofighter Typhoon – The world's most advanced fighter aircraft |url=http://www.eurofighter.com/capabilities/technology/voice-throttle-stick/direct-voice-input.html |url-status=live |archive-url=https://web.archive.org/web/20130511025203/http://www.eurofighter.com/capabilities/technology/voice-throttle-stick/direct-voice-input.html |archive-date=11 May 2013 |access-date=1 May 2018 |website=www.eurofighter.com |df=dmy-all}}</ref>

Speaker-independent systems are also being developed and are under test for the [[Lockheed Martin F-35 Lightning II|F-35 Lightning II]] (JSF) and the [[Alenia Aermacchi M-346 Master]] lead-in fighter trainer. These systems have produced word accuracy scores in excess of 98%.<ref>{{Cite web |last=Schutte |first=John |date=15 October 2007 |title=Researchers fine-tune F-35 pilot-aircraft speech system |url=https://www.af.mil/News/story/id/123071861/ |url-status=live |archive-url=https://web.archive.org/web/20071020030310/http://www.af.mil/news/story.asp?id=123071861 |archive-date=20 October 2007 |publisher=United States Air Force}}</ref>

====Helicopters====
The problems of achieving high recognition accuracy under stress and noise are particularly relevant in the [[helicopter]] environment as well as in the jet fighter environment. The acoustic noise problem is actually more severe in the helicopter environment, not only because of the high noise levels but also because the helicopter pilot, in general, does not wear a [[Fighter pilot helmet|facemask]], which would reduce acoustic noise in the [[microphone]]. Substantial test and evaluation programs have been carried out in the past decade in speech recognition systems applications in helicopters, notably by the [[U.S. Army]] Avionics Research and Development Activity (AVRADA) and by the Royal Aerospace Establishment ([[Royal Aircraft Establishment|RAE]]) in the UK. Work in France has included speech recognition in the [[Puma helicopter]]. There has also been much useful work in [[Canada]]. Results have been encouraging, and voice applications have included: control of communication radios, setting of [[navigation]] systems, and control of an automated target handover system.

As in fighter applications, the overriding issue for voice in helicopters is the impact on pilot effectiveness. Encouraging results are reported for the AVRADA tests, although these represent only a feasibility demonstration in a test environment. Much remains to be done both in speech recognition and in overall [[speech technology]] in order to consistently achieve performance improvements in operational settings.

====Training air traffic controllers====
Training for air traffic controllers (ATC) represents an excellent application for speech recognition systems. Many ATC training systems currently require a person to act as a "pseudo-pilot", engaging in a voice dialog with the trainee controller, which simulates the dialog that the controller would have to conduct with pilots in a real ATC situation. Speech recognition and [[speech synthesis|synthesis]] techniques offer the potential to eliminate the need for a person to act as a pseudo-pilot, thus reducing training and support personnel. In theory, Air controller tasks are also characterized by highly structured speech as the primary output of the controller, hence reducing the difficulty of the speech recognition task should be possible. In practice, this is rarely the case. The FAA document 7110.65 details the phrases that should be used by air traffic controllers. While this document gives less than 150 examples of such phrases, the number of phrases supported by one of the simulation vendors speech recognition systems is in excess of 500,000.

The USAF, USMC, US Army, US Navy, and FAA as well as a number of international ATC training organizations such as the Royal Australian Air Force and Civil Aviation Authorities in Italy, Brazil, and Canada are currently using ATC simulators with speech recognition from a number of different vendors.{{citation needed|date=December 2012}}

===Telephony and other domains===
ASR is now commonplace in the field of [[telephony]] and is becoming more widespread in the field of [[computer gaming]] and simulation. In telephony systems, ASR is now being predominantly used in contact centers by integrating it with [[IVR]] systems. Despite the high level of integration with word processing in general personal computing, in the field of document production, ASR has not seen the expected increases in use.

The improvement of mobile processor speeds has made speech recognition practical in [[smartphone]]s. Speech is used mostly as a part of a user interface, for creating predefined or custom speech commands.

=== People with disabilities ===
People with disabilities can benefit from speech recognition programs. For individuals that are Deaf or Hard of Hearing, speech recognition software is used to automatically generate a closed-captioning of conversations such as discussions in conference rooms, classroom lectures, and/or religious services.<ref>{{Cite web |date=18 March 2010 |title=Overcoming Communication Barriers in the Classroom |url=http://www.massmatch.org/aboutus/listserv/2010/2010-03-31.html |url-status=usurped |archive-url=https://web.archive.org/web/20130725024622/http://www.massmatch.org/aboutus/listserv/2010/2010-03-31.html |archive-date=25 July 2013 |access-date=15 June 2013 |publisher=MassMATCH |df=dmy-all}}</ref>

Students who are blind (see [[Blindness and education]]) or have very low vision can benefit from using the technology to convey words and then hear the computer recite them, as well as use a computer by commanding with their voice, instead of having to look at the screen and keyboard.<ref name="brainline">{{Cite web |year=2010 |title=Speech Recognition for Learning |url=http://www.brainline.org/content/2010/12/speech-recognition-for-learning_pageall.html |url-status=live |archive-url=https://web.archive.org/web/20140413100513/http://www.brainline.org/content/2010/12/speech-recognition-for-learning_pageall.html |archive-date=13 April 2014 |access-date=26 March 2014 |publisher=National Center for Technology Innovation |df=dmy-all}}</ref><!--second host of same journal, if this becomes deadlink: http://www.ldonline.org/article/38655/ -->

Students who are physically disabled have a [[Repetitive strain injury]]/other injuries to the upper extremities can be relieved from having to worry about handwriting, typing, or working with scribe on school assignments by using speech-to-text programs. They can also utilize speech recognition technology to enjoy searching the Internet or using a computer at home without having to physically operate a mouse and keyboard.<ref name="brainline" />

Speech recognition can allow students with learning disabilities to become better writers. By saying the words aloud, they can increase the fluidity of their writing, and be alleviated of concerns regarding spelling, punctuation, and other mechanics of writing.<ref>{{Cite web |last1=Follensbee |first1=Bob |last2=McCloskey-Dale |first2=Susan |year=2000 |title=Speech recognition in schools: An update from the field |url=http://www.csun.edu/~hfdss006/conf/2000/proceedings/0219Follansbee.htm |url-status=live |archive-url=https://web.archive.org/web/20060821213145/http://www.csun.edu/~hfdss006/conf/2000/proceedings/0219Follansbee.htm |archive-date=21 August 2006 |access-date=26 March 2014 |website=Technology And Persons With Disabilities Conference 2000 |df=dmy-all}}</ref> Also, see [[Learning disability]].

The use of voice recognition software, in conjunction with a digital audio recorder and a personal computer running word-processing software has proven to be positive for restoring damaged short-term memory capacity, in stroke and craniotomy individuals.

Speech recognition is also very useful for people who have difficulty using their hands, ranging from mild repetitive stress injuries to involve disabilities that preclude using conventional computer input devices. In fact, people who used the keyboard a lot and developed [[Repetitive Strain Injury|RSI]] became an urgent early market for speech recognition.<ref>{{Cite web |title=Speech recognition for disabled people |url=http://www.businessweek.com/1998/08/b3566022.htm |url-status=dead |archive-url=https://web.archive.org/web/20080404013302/http://www.businessweek.com/1998/08/b3566022.htm |archive-date=4 April 2008 |df=dmy-all}}</ref><ref>[[Friends International Support Group]]</ref> Speech recognition is used in [[deaf]] [[telephony]], such as voicemail to text, [[relay services]], and [[Telecommunications Relay Service#Captioned telephone|captioned telephone]]. Individuals with learning disabilities who have problems with thought-to-paper communication (essentially they think of an idea but it is processed incorrectly causing it to end up differently on paper) can possibly benefit from the software but the technology is not bug proof.<ref>{{Cite journal |last=Garrett |first=Jennifer Tumlin |display-authors=etal |year=2011 |title=Using Speech Recognition Software to Increase Writing Fluency for Individuals with Physical Disabilities |url=https://scholarworks.gsu.edu/epse_diss/46 |journal=Journal of Special Education Technology |volume=26 |issue=1 |pages=25–41 |doi=10.1177/016264341102600104 |s2cid=142730664 |access-date=9 September 2024 |archive-date=9 September 2024 |archive-url=https://web.archive.org/web/20240909053848/https://scholarworks.gsu.edu/epse_diss/46/ |url-status=live }}</ref> Also the whole idea of speak to text can be hard for intellectually disabled person's due to the fact that it is rare that anyone tries to learn the technology to teach the person with the disability.<ref>Forgrave, Karen E. "Assistive Technology: Empowering Students with Disabilities." Clearing House 75.3 (2002): 122–6. Web.</ref>

This type of technology can help those with dyslexia but other disabilities are still in question. The effectiveness of the product is the problem that is hindering it from being effective. Although a kid may be able to say a word depending on how clear they say it the technology may think they are saying another word and input the wrong one. Giving them more work to fix, causing them to have to take more time with fixing the wrong word.<ref>{{Cite journal |last1=Tang |first1=K. W. |last2=Kamoua |first2=Ridha |last3=Sutan |first3=Victor |year=2004 |title=Speech Recognition Technology for Disabilities Education |journal=Journal of Educational Technology Systems |volume=33 |issue=2 |pages=173–84 |citeseerx=10.1.1.631.3736 |doi=10.2190/K6K8-78K2-59Y7-R9R2 |s2cid=143159997}}</ref>

===Further applications===
*[[Aerospace]] (e.g. [[space exploration]], [[spacecraft]], etc.) NASA's [[Mars Polar Lander]] used speech recognition technology from [[Sensory, Inc.]] in the Mars Microphone on the Lander<ref name="Planetary Society article">{{Cite web |title=Projects: Planetary Microphones |url=http://www.planetary.org/programs/projects/planetary_microphones/mars_microphone.html |url-status=dead |archive-url=https://web.archive.org/web/20120127161038/http://www.planetary.org/programs/projects/planetary_microphones/mars_microphone.html |archive-date=27 January 2012 |publisher=The Planetary Society}}</ref>
*Automatic [[Same language subtitling|subtitling]] with speech recognition
*Automatic [[emotion recognition]]<ref>{{Cite book |last1=Caridakis |first1=George |title=Artificial Intelligence and Innovations 2007: From Theory to Applications |last2=Castellano |first2=Ginevra |last3=Kessous |first3=Loic |last4=Raouzaiou |first4=Amaryllis |last5=Malatesta |first5=Lori |last6=Asteriadis |first6=Stelios |last7=Karpouzis |first7=Kostas |date=19 September 2007 |publisher=Springer US |isbn=978-0-387-74160-4 |series=IFIP the International Federation for Information Processing |volume=247 |pages=375–388 |language=en |chapter=Multimodal emotion recognition from expressive faces, body gestures and speech |doi=10.1007/978-0-387-74161-1_41}}</ref>
*Automatic [[Shot (filmmaking)|shot]] listing in audiovisual production
*[[Automatic translation]]
*[[eDiscovery]] (Legal discovery)
*[[Hands-free computing]]: Speech recognition computer [[user interface]]
*[[Home automation]]
*[[Interactive voice response]]
*[[Mobile telephony]], including mobile email
*[[Multimodal interaction]]<ref name="interspeech2014Keynote" />
*Real Time [[Captioning]]<ref>{{Cite web |title=What is real-time captioning? {{!}} DO-IT |url=https://www.washington.edu/doit/what-real-time-captioning |access-date=2021-04-11 |website=www.washington.edu |archive-date=9 September 2024 |archive-url=https://web.archive.org/web/20240909054510/https://www.washington.edu/doit/what-real-time-captioning |url-status=live }}</ref>
*[[Robotics]]
*Security, including usage with other biometric scanners for [[multi-factor authentication]]<ref>{{Cite book |last1=Zheng |first1=Thomas Fang |url=http://link.springer.com/10.1007/978-981-10-3238-7 |title=Robustness-Related Issues in Speaker Recognition |last2=Li |first2=Lantian |date=2017 |publisher=Springer Singapore |isbn=978-981-10-3237-0 |series=SpringerBriefs in Electrical and Computer Engineering |location=Singapore |doi=10.1007/978-981-10-3238-7 |access-date=9 September 2024 |archive-date=9 September 2024 |archive-url=https://web.archive.org/web/20240909053948/https://link.springer.com/book/10.1007/978-981-10-3238-7 |url-status=live }}</ref>
*Speech to text (transcription of speech into text, real time video [[captioning]], Court reporting )
*[[Telematics]] (e.g. vehicle Navigation Systems)
*[[Transcription (linguistics)|Transcription]] (digital speech-to-text)
*[[Video games]], with ''[[Tom Clancy's EndWar]]'' and ''[[Lifeline (video game)|Lifeline]]'' as working examples
*[[Virtual assistant (artificial intelligence)|Virtual assistant]] (e.g. [[Apple Siri|Apple's Siri]])