Speech Recognition and Its Impact on the Fast Food Sector: A Study

This report provides a detailed analysis of the impact of speech recognition technology on the fast food industry. It begins with an executive summary and table of contents, followed by an introduction that sets the context. The research section defines the industry and technology, highlighting the use of speech recognition for smarter assistance and AI-based solutions. The brainstorming section explores how the technology affects the industry, including time optimization, consumer engagement, and AI-based chatbots. The report then addresses regulations and ethics, covering food safety, supply chain costs, traceability, and ethical food practices. It identifies process components like speech capturing devices and digital signal processors. The analysis includes business process model diagrams to illustrate the disruption of changes. The conclusion synthesizes the advancements in speech synthesis and suggests areas for further development, referencing multiple sources.

Running head: SPEECH RECOGNITION AND FAST FOOD INDUSTRY
Speech Recognition and Fast Food Industry
Name of the student:
Name of the university:
Author Note

1SPEECH RECOGNITION AND FAST FOOD INDUSTRY
Executive summary
In this analysis the impacts of speech recognition on the fast food area is found out. Further, the
technology and the industry’s scenario is been defined. Further, the brainstorming concepts are
discussed. Then various regulations are investigated. At last, the process components are been
defined.

2SPEECH RECOGNITION AND FAST FOOD INDUSTRY
Table of Contents
Introduction:..........................................................................................................................................3
Part 1: Research:....................................................................................................................................3
Description of industry and technology:............................................................................................3
Part 2: Braining storming:.....................................................................................................................4
Ways in which the technology has affected the industry:.................................................................4
Part 3: Regulation and Ethics:...............................................................................................................5
Description of regulations:................................................................................................................5
Regulations on developing the safety and food quality:................................................................5
Regulations on driving the costs out of supply chain:...................................................................5
Regulations on developing lots of traceability and tracking:........................................................5
Regulations on providing ethical food or maintaining food ethics:..............................................6
Part 4: Disruption:..................................................................................................................................6
Identifying process components:.......................................................................................................6
Conclusion:............................................................................................................................................9
References:..........................................................................................................................................11

3SPEECH RECOGNITION AND FAST FOOD INDUSTRY
Introduction:
People talk over phones, computers and their devices, and here, the speech recognition
technology utilizes what is said. It also triggers the actions. The innovation has been used for
replacing the measures such as selection, clicking and typing in various ways. This involves the
computational linguistics developing technologies and methodologies.
In the following study the effects of speech recognition over the fast food sector is
determined. At first the industry and technology is defined here. Next, brainstorming ideas are
demonstrated. Next different regulations are analyzed. Lastly, the process components are defined.
Part 1: Research:
Description of industry and technology:
The speech recognition is helpful to obtain smarter assistances to plan and cook the healthy
meals. Apart from this, the entire expense of implementation comprises of AI-based solutions. This
is decreasing day by day. In this way, the brands are able to tap into various scopes that s resented by
speech recognition technology. In this way, the AI-powered has comprise the home-cooking
sidekick. The product empowered by AI is the power to make millennial in eating healthier items. It
is seen that the home assistant is been controlled with the help of vice technology. It uses the AI in
addressing the needs of the kitchen (Liu et al., 2016). Apart from this, the device is able to plan
various weekly meals. This is as involves the dietary preferences. It also helps in demonstrating the
cooking tutorials over its screen, organize the list of shopping, supervise the pantry and make
arrangement of grocery delivery (Wang et al. 2019).

4SPEECH RECOGNITION AND FAST FOOD INDUSTRY
Part 2: Braining storming:
Ways in which the technology has affected the industry:
First of all, the enterprises has been leveraging image recognition and speech for time
optimization and engagement of consumers. Here, Lark is a popular application. It has been acting as
the personal coach of diet. It has been using the speech recognition to the log meals. The users have
required to have simple voice log about what they have been eating over the app. Here, the users
have the simple voice log regarding what have been on the application (Farooq and Sazonov 2016).
Hence, they are able to estimate the quantity of calories intake. The track of nutrition is another same
type of voice-activated application that has been showing the promise.
Secondly, the business has been rousingly depending on the machine learning techniques and
NLP. This is gain more advice and products that are personalized. After thorough analysis, the
Lifesum has found out that the London consumers has been suffering from stress and fatigue. This is
because of insufficient levels of omega-3, Zinc and Vitamin E (Alshurafa et al. 2015).
Thirdly, the AI based chatbots has been rising the user engagement. As the chatbots has been
augmented with AI. They has been able to become cost-effective and responsive customer service.
This is for limitless audiences of messaging platforms. For example, the Whole Foods has found to
design the chatbot over Facebook Messenger. It is seen that the chatbot has been displaying the
products and various recipes. This has been based on various specialized diets and particular
ingredients and cuisines for the consumers. In this way, the chatbot development is a good strategy
for kick-starting the project in food industry. This indicates the AI on the base of the technology
(Verdouw et al. 2016).

5SPEECH RECOGNITION AND FAST FOOD INDUSTRY
Part 3: Regulation and Ethics:
Description of regulations:
Regulations on developing the safety and food quality:
The worldwide food chain and the sector of farm-to-table has gained to various unwanted
publicity. This is because of the high-profile cases of food borne illness. It is the sector where the AI
has been really shining the food supply chain. It is the sector where the rates of the AI adoption is
seen under the beverage and food industry.
Regulations on driving the costs out of supply chain:
As the beverage and food business has been developing the serviceability, security and
quantity they are able to rise the efficiency. In turn this has been decreasing the costs of operations
of the entire supply chain (Bronson and Knezevic 2016). It has been driving higher profitability for
the business. The food processing requirements can be considered that needs constant upkeep and
repairs.
Regulations on developing lots of traceability and tracking:
This involves the product silage, illness that is foodborne and additional issues that are
related to foods. They must adhere to the myriad regulations of government. This surrounded across
the distribution and manufacturing of food. This is at the mind for beverage and food distributors
(Brem-Wilson 2015).

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Regulations on providing ethical food or maintaining food ethics:
This indicates the moral outcomes of the choice of foods. These are made by the human
beings and for food animals. The common issues are the harms to the scenarios. This consists of
exploitive labor practices, strategies of foods, inhuman treatment of food animals and unexpected
impacts of policy of foods.
Part 4: Disruption:
Identifying process components:
Speech capturing device:
This comprises of microphone. This can convert the sound waves to electrical signals.
Digital signal module and processor:
This performs the processing of raw speech signal such as frequent domain conversion and
restoring the needed data.
Preprocessed signal storage:
This is stored under in the memory for carrying the further activates of speech recognition.
Reference speech patterns:
Here, the system comprises of the pre-defined patterns of speeches or template. These are
stored already in memory and used for the reference speech pattern for finding the real words and
patterns of words (Malmaud et al. 2015).
Illustrating the disruption of changes:

7SPEECH RECOGNITION AND FAST FOOD INDUSTRY
This is analyzed through the following business process model diagram.
Figure 1: Issues of hospitality industry
(Source: Created by the author)

8SPEECH RECOGNITION AND FAST FOOD INDUSTRY
Figure 2: “Effect of using AI in hospitality industry
(Source: Created by the author)

9SPEECH RECOGNITION AND FAST FOOD INDUSTRY
Figure 1: “Diagram supporting the description”
(Source: Created by Author)
Conclusion:
The synthesis of speech id quickly developed in steady manner since the past decades. This
has been included to various new applications. It is seen that most of the applications, there is
comprehensibility an intelligibility o synthetic speeches. These are found to be reaching an
acceptable level. Nonetheless, in prosodic, the pronunciation areas and preprocessing of text has
been under much work. Developments are required to be done for gaining more natural speech of

10SPEECH RECOGNITION AND FAST FOOD INDUSTRY
sounding. The natural speech has various dynamic change. Its naturalness has been impossible to
gain. It must be reminded that natural speech has various dynamic changes. Its perfect naturalness is
impossible to gain. Nonetheless, as the market of synthesis of speech has been relating to
applications has been rising in steady manner, the interest for providing refunds and efforts the
research sector has been rising.

11SPEECH RECOGNITION AND FAST FOOD INDUSTRY
References:
Alshurafa, N., Kalantarian, H., Pourhomayoun, M., Liu, J.J., Sarin, S., Shahbazi, B. and Sarrafzadeh,
M., 2015. Recognition of nutrition intake using time-frequency decomposition in a wearable
necklace using a piezoelectric sensor. IEEE Sensors Journal, 15(7), pp.3909-3916.
Brem-Wilson, J., 2015. Towards food sovereignty: interrogating peasant voice in the United Nations
Committee on World Food Security. Journal of Peasant Studies, 42(1), pp.73-95.
Bronson, K. and Knezevic, I., 2016. Big Data in food and agriculture. Big Data & Society, 3(1),
p.2053951716648174.
Farooq, M. and Sazonov, E., 2016. A novel wearable device for food intake and physical activity
recognition. Sensors, 16(7), p.1067.
Liu, C., Cao, Y., Luo, Y., Chen, G., Vokkarane, V. and Ma, Y., 2016, May. Deepfood: Deep
learning-based food image recognition for computer-aided dietary assessment. In International
Conference on Smart Homes and Health Telematics (pp. 37-48). Springer, Cham.
Malmaud, J., Huang, J., Rathod, V., Johnston, N., Rabinovich, A. and Murphy, K., 2015. What's
cookin'? interpreting cooking videos using text, speech and vision. arXiv preprint arXiv:1503.01558.
Verdouw, C.N., Wolfert, J., Beulens, A.J.M. and Rialland, A., 2016. Virtualization of food supply
chains with the internet of things. Journal of Food Engineering, 176, pp.128-136.
Wang, J., Chen, Y., Hao, S., Peng, X. and Hu, L., 2019. Deep learning for sensor-based activity
recognition: A survey. Pattern Recognition Letters, 119, pp.3-11.