ENGL 203: Speech Recognition Technology and Its Benefits Analysis

This report analyzes the benefits of speech recognition technology, focusing on improvements in reliability and time-saving aspects, futuristic applications like talking to robots and controlling digital devices, and its helpfulness in daily life through hands-free technology and aiding the hearing and visually impaired. The document references various studies and examples to support its claims, highlighting the increasing importance and integration of speech recognition in various sectors including healthcare, security, and consumer electronics. It also discusses the evolution of speech recognition from its early limitations to its current widespread use and potential for future advancements. Desklib provides students access to similar solved assignments and resources.

Running head: SPEECH RECOGNITION TECHNOLOGY
SPEECH RECOGNITION TECHNOLOGY
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SPEECH RECOGNITION TECHNOLOGY 1
Term Paper Outline
Major: Speech recognition
Research Question: What are the benefits of speech recognition
technology?
Aim: The aim of this paper is to analyse the benefits of speech
recognition technology.
Type of Focus: Benefits.
Introduction:
The speech recognition technology for the reporting of medical have
been available for commercially over 2 decades. Speech recognition is
one input mechanism that is available for assisting with the
documentation that is for clinical by transferring and translating the
speech into text format or by controlling the functions of user interface
verbally. The speech recognition technology has been successfully
adopted in the clinical setting like report of radiology dictation where,
speech recognition has been used in the conjunction, by achieving the
picture or system of radio information as well as the communication
system. In the contrast, speech recognition is currently used widely in a
huge number of consumer applications with including applications having
question answering capability and interface control in the smart phones
(Hodgson & Coiera, 2015). The early adoption of the documentation based
on speech recognition was impeded by the technology that is immature as
well as the error rates that are clinically unacceptable. However, the

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steady advances in the design of recognition algorithm as well as in the
performance of the system have been created over last 20 years. Speech
recognition in the documentation related to clinic has been improved over
time.
Main Points:
I. Improvement:
a. Reliability improvement:
i. At present, the voice recognition systems are very much
reliable that, the software is used widely in the services
that is related to health, security industry, legal
profession as well as the military for naming a few
(Ranchal et al., 2013)..
ii. Response to the evidence:
Currently this is a common practice for the doctors for
dictating the case notes of him to be converted to paper
or digital documents for use in later.
b. Time saving:
i. On average the dictating is three times faster than the
typing so at the time of deadlines and essence are
looming that makes sense for resorting to the methods
which shall help for speeding this things up (Ranchal et
al., 2013).
ii. Response to the evidence
Specially this is so helpful at the time of transcribing of
interviews which has the ability of saving time a lot.
II. Futuristic:
a. Talking to the robots:
i. Speaking with the robots is not an activity that is
common. However, the robots are being employed
increasingly in every roles once that was performed by

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the humans including interface and conversation. The
firms are exploring already using software and robots
for performing interviews that are initial (Zhang et al.,
2018).
ii. Response to the evidence
It is essential that robots can interpret in what is being
said by the interviewee because the interviews must
have to be conversational. However, every time usage
of robots in the fields can not be helpful.
b. Controlling Digital devices:
i. The digital assistant that are using for personal benefits,
such as the Google home and Alexa require
communication verbally between the computers and
humans. (Zhang et al., 2018).
ii. Response to the evidence
The digital voice recognition devices are also some
great examples of, how the machine learning is using
into the computers for better understanding of speech
of the human. The smart assistants are becoming
smarter day after day as well as the devices are also
getting engaged with daily life of the human.
III. Helpful for daily life:
a. Enabling Hands free technology:
i. At the time when the hands and eyes of the human is
busy, like when someone is driving then this technology
is so much helpful. (Herbordt et al., 2005)
ii. Response to the evidence
Being having the ability of communicating with the
Google maps or apple Siri is so helpful for the users that
they can navigate using their smartphone while
travelling to anywhere. It also helps the human to do a

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task via voice recognition by providing a hands free
environment.
b. Aiding the hearing and visually impaired
i. There are so many people with the impairment of visually who are
relying on the screen readers as well as the dictation systems related to
Speech to speech. It can be very critical tool for communication for the
hearing impaired by converting the audios to text format (Akeroyd,
2014).
ii. Response to the evidence
Generally, the listeners of hearing impaired do less well in the
experiments that measures the localization performance than the
normal hearing listeners. It is so much helpful for the daily life of the
human.
References
Akeroyd, M. A. (2014). An overview of the major phenomena of the
localization of sound sources by normal-hearing, hearing-impaired,
and aided listeners. Trends in hearing, 18, 2331216514560442.
Convery, E., Keidser, G., Hartley, L., Caposecco, A., Hickson, L., & Meyer,
C. (2011). Management of hearing aid assembly by urban-dwelling

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hearing-impaired adults in a developed country: Implications for a
self-fitting hearing aid. Trends in Amplification, 15(4), 196-208.
Edwards, B. (2007). The future of hearing aid technology. Trends in
amplification, 11(1), 31-45.
https://doi.org/10.1177/1084713806298004
Herbordt, W., Horiuchi, T., Fujimoto, M., Jitsuhiro, T., & Nakamura, S.
(2005, November). Hands-free speech recognition and
communication on PDAs using microphone array technology. In IEEE
Workshop on Automatic Speech Recognition and Understanding,
2005. (pp. 302-307). IEEE.
Hodgson, T., & Coiera, E. (2015). Risks and benefits of speech recognition
for clinical documentation: a systematic review. Journal of the
american medical informatics association, 23(e1), e169-e179.
Ranchal, R., Taber-Doughty, T., Guo, Y., Bain, K., Martin, H., Robinson, J.
P., & Duerstock, B. S. (2013). Using speech recognition for real-time
captioning and lecture transcription in the classroom. IEEE
Transactions on Learning Technologies, 6(4), 299-311.
Zhang, N., Mi, X., Feng, X., Wang, X., Tian, Y., & Qian, F. (2018).
Understanding and mitigating the security risks of voice-controlled
third-party skills on amazon alexa and google home. arXiv preprint
arXiv:1805.01525.

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