TITLE of Policy Position Paper:

Call for Further Research on the Effectiveness of Voice Recognition Technology by January 2019 on behalf of Australian Technology Park (ATP)

Technological Issue:

Technology has had a lot of impact especially in the contemporary world where technology has become a vital part of our lives. However, technology has its advantages and disadvantages raising questions on its essence (MacKenzie & Wajcman, 1999). For instance, almost all aspects of the modern world have adopted technology to automate many operations (Cech, 2014). From manufacturing to education, automation is of great importance making things run faster and effectively. There is concern among many people regarding how technology has impacted on the business world environment, sustainability and social change (Vanderburg, 2005). For example, voice recognition is gradually being implemented in many authentication systems due to the uniqueness of an individual’s voice. This means better ways of authentication are a head of us in the near future. But how does this impact on the society’s social change. This paper proposes the need for more research on voice recognition as a form of authenticating people.

Voice recognition is the procedure by which a program gets and deciphers transcription and also comprehends and completes spoken commands. Voice recognition innovations enable clients to consent to protection, security, and wellbeing necessities represented by law and by client desires. Programmed voice recognition and content to-speech programming cooperate to voice-empower numerous applications. Programming deals are relied upon to increment at a significant rate amid this period (Anniss, 2014). Voice has developed as a feasible authentication technique on the grounds that voice is a unique normal for an individual simply like fingerprints or iris. What sets voice separated as a favoured validation technique for online exchanges is its capacity to join forces with the phone to convey to a great degree exact remote verification.

Theoretical Concepts:

Researchers have consolidated speech processing, characteristic language processing, and data recovery advances to address operational needs in genuine applications. Thus, speech data processing progressively has been multidisciplinary. Speech data processing principally includes automated speech recognition (ASR), text to-speech (TTS) combination, acknowledgment of other valuable parts of speech, and above all, all parts of ensuing processing of the yields of these frameworks for representative data centered applications, for example, interpretation, semantic comprehension, and positioned data recovery. One essential potential application is for discourse with organized or even unstructured databases, where individuals can collaborate promptly with PCs over different correspondence joins or with gadgets straightforwardly.

All techniques specified above are performed by machine learning given the many-sided quality behind the cepstral highlights and the absence of a conspicuous way to relate the highlights to a specific phoneme (Mitcham, 1994). In this way, the phonetic recognizer is consequently prepared in light of some phoneme marks and its execution typically builds the additionally preparing information or parameters are utilized. However, information driven technique may not be the ideal method to proceed enhancing execution (Anniss, 2014; Gifford, 2013). To appropriately build phoneme acknowledgment what’s more, show signs of improvement understanding in the activity of a specific recognizer, it is important to distinguish under which conditions it comes up short. For example, a telephone having a place with a specific phoneme might be befuddled to another phoneme given some digestion impacts. Some phonetic recognizer can manage digestion impacts however some other not.

The study will focus on why there is need to conduct more research on voice recognition to make them effective. Maybe a couple of the voice recognition programs do not have the capacity to comprehend the setting of the language to decipher the substance in its actual sense. With restricted interpreting abilities, these programs may prompt blunders causing perplexity and confusion of actualities (Pieraccini, 2012). There can be various open doors rising up out of broadening the scope of Internet of Things; which would profit various generally disconnected gadgets from inventive methods for client connections notwithstanding the customary means, for example, touch screens.

Stakeholders:

The Australian Communications Authority is the main stakeholder in terms of controlling technological advancements in speech processing. Other stakeholders include Australian Technology Park (ATP) and the Professional Association for Australia’s ICT sector. Increased interest in speech-based biometric frameworks for multifaceted validation is one of the significant driver for the development in the market, while lack of accuracy in recognizing speech in local highlight can be a limiting component in the market (Mowjoon, 2018; Ferneding, 2003). Developing selection of AI based advancements and contribution of speech recognition in AI will get new open doors the market in coming years. Human voice examples or iris recognition require not be thought to be unique to be valuable apparatuses for securing private access to our financial balances. Similarly, fingerprints require not be thought to be unique to be valuable in courts. Risks and errors happen when a lot of is asserted for such methods.

Research plan

The research will take seven months, it will focus on Call for Further Research on the Effectiveness of Voice Recognition Technology. The data collection process will take two months. It will give enough time to locate necessary resources. Data will be gathered from books, journal articles such as journal of applied research and technology. The next step after data collection will be drafting the research. The process will take two months. Next, the research will be edited, and the proofreading will take two months.

References

Anniss, M. (2014). How Does Voice Recognition Work?. New York, NY : Gareth Stevens Publishing

Bottom of Form

Baillie, C., Pawley, A., and Riley, D. (eds.). (2012). Engineering and Social Justice in the University and Beyond. West Lafayette, Ind.: Purdue University Press.

Cech, E. A., (2014). “Culture of Disengagement in Engineering Education” Science, Technology and Human Values 39/1:42-72

MacKenzie, D. & Wajcman, J., (1999) ‘Introductory Essay: the social shaping of technology’, in

MacKenzie, D. & Wajcman, J. (Eds.) The Social Shaping of Technology, 2nd ed., Open University Press, Buckingham, pp. 3 – 27.

Mitcham, C., (1994). ‘The Philosophical Questioning of Technology’, in Thinking through

technology: The path between engineering and philosophy, University of Chicago Press, Chicago, pp. 94 – 113.

Pieraccini, R. (2012). The voice in the machine: Building computers that understand speech. Cambridge, Mass: MIT Press.

Ferneding, K. A. (2003). Questioning technology: Electronic technologies and educational reform. New York: P. Lang.

Top of Form

Gifford, C. (2013). Technology. New York: Scholastic.

Mowjoon, M. (2018). Interrogating Technology: Sustainability, Environment and Social Change. University of Technology Sydney.

Vanderburg, W.H., (2005). ‘Introduction: Where are we going with technology?’, in Living in

The Labyrinth of Technology, University of Toronto Press, Toronto, pp. 3 – 14.