Abstract
The AI voice assistant mobile application was developed to aid drivers in operating their mobile phones while driving without touching their phones. The literature review examines multiple innovative artificial technologies involved in applications with voice assistants in natural language processing (NLP) techniques. The methodology used involved a qualitative approach, and the design science paradigm was used for the development of the voice assistant for smartphones with NLP techniques. NLP techniques that were applied in the development of the AI voice assistant are smart synthesis, data flow sequence, core and interface accessing, part of speech tagging, named entity recognition, conference resolution, and porter stemming. Some of the operations that are achieved by the application include arithmetic calculations based on voice commands and returning the computer result via voice, searching the internet based on user voice input, and providing a response via voice assistance.
Top1. Introduction
In the development process of AI voice assistants, data analytics techniques may be employed to analyze user interactions, feedback, and usage patterns. This data analysis can help optimize the performance and functionality of the voice assistant, ensuring that it effectively understands and responds to user commands and queries. AI voice assistants can be integrated into marketing strategies and customer engagement initiatives. For example, voice assistants can facilitate personalized marketing experiences by providing product recommendations, answering customer queries, and delivering targeted promotions based on user interactions and preferences. Effective use of AI voice assistants can contribute to enhancing user experience and engagement, which are key objectives in marketing research and customer relationship management. By providing seamless and intuitive voice-based interactions, businesses can foster stronger connections with customers and drive brand loyalty. Both AI-driven marketing research and the development of AI voice assistants involve considerations of data privacy and security. Ensuring compliance with regulations such as GDPR (General Data Protection Regulation) and implementing robust security measures are critical aspects of deploying AI technologies in both domains.
A voice assistant is a system that listens to voice commands and responds with pertinent information (Lawswood Claims LTD, 2019), as well as carrying out tasks as asked by the user. The main goal of the AI voice assistant for smartphones is to lessen the number of accidents that are due to physical interaction with smartphones while driving. Driver distraction is increasingly recognized as a significant source of injuries and fatalities on the roads (Hoy, 2018). Distraction can arise from visual or manual interference, for example, when a driver takes his or her eyes off the road to interact with a smartphone (Strayer, 2015). Concern over inattentive driving due to the use of smartphones is growing as more and more smartphones are being used while driving. According to a report by the National Safety Council of the United Kingdom in 2019, mobile phone usage while driving caused 1.6 million car accidents worldwide (Lawswood Claims LTD, 2019). More than 390,000 people in the United Kingdom received injuries in accidents caused by the use of smartphones while driving (Fox, 2012). It is against the law to use a smartphone or any other type of phone while operating a motor vehicle, not only in Zimbabwe but also in other countries as well. It is illegal to use a phone while operating a vehicle under Section 116 of the Zimbabwe Statutory Instrument 199 of 2000. People who are able to use their voice to input a command are able to use the voice assistant application. In today’s society, there are an increasing number of activities that may be carried out online, including texting, buying food, and purchasing train tickets. Nearly all of these internet services require user input, and this can be achieved through the use of smartphones. The application of natural language processing (NLP) techniques can be found in many fields, including healthcare and finance, among others. NLP techniques are used to develop intelligent virtual assistants, chatbots, and search engines that can comprehend the real world. They also have applications in many state-of-the-art technologies like sentiment analysis, fraud detection, and language translation.
Key Terms in this Chapter
AI Voice Assistant: An AI-powered software application that uses speech recognition and technology to interact with users, understand their commands or questions, and provide relevant or functional responses.
Natural Language Processing (NLP): A branch of artificial intelligence focused on enabling computers to understand, interpret, and respond to human speech in context. NLP technology includes tasks such as speech recognition, language understanding and language production.
Text-to-Speech (TTS): Technology that converts text to speech. It allows the intelligent sound engineer to give a voice response to the user.
Natural Language Understanding (NLU): The process of analyzing and interpreting human speech to extract meaning and determine intent. NLU technology can help AI voice assistants understand user commands or questions.
Wake Words/Hot Words: Special words or phrases that enable the AI voice assistant to start listening to and executing user commands. Examples include “Hey Siri,” “OK Google,” or “Alexa.”
Intent Determination: The process of determining the intent or purpose behind a user's command or question. AI voice helps use objective recognition technology to understand the actions the user wants to perform.
Speech Recognition: Technology that converts speech into text. It enables intelligent voice recognition to help understand what the user is saying.
Location Extraction: The process of identifying and extracting specific information from user input. Fields can be names, dates, locations, or other relevant information that the AI voice assistant needs to understand and process.
Voice Biometrics: Use a unique voice to identify and identify a person. AI voice assistants can use voice biometrics to provide personal identification and security.
Context Comprehension: The ability of intelligent people to understand and remember previous interactions or messages during a conversation. It allows for more coordinated and personalized responses.
Conversation Control: It is a part of the artificial intelligence voice assistant used to control the conversation process between the user and the assistant. It monitors content, performs rotations, and determines how the assistant responds to user input.
Sentiment Analysis: A technique used to determine the mood or voice of mind behind the user's ideas. It can help voice intelligence experts determine whether a user is showing positive, negative, or negative feedback.