The term “Artificial intelligence” is widely used in many sectors, starting from finance and tech to biotech and medicine. But do you understand what does it mean and what stands behind this term? In this article, we will explain what artificial intelligence really is and how it is related to voice recognition topic. 

Understanding AI 

Merely talking, artificial intelligence refers to the simulation of human intelligence in machines programmed to think like humans and mimic their actions. The ideal characteristic of artificial intelligence is its ability to rationalize and take steps that have the best chance of achieving a specific goal.  

When most people hear the term artificial intelligence, the first thing they usually think of is robots. But it is far away from the truth. Artificial intelligence is based on the principle that human intelligence can be defined so that a machine can easily mimic it and execute tasks, from the simplest to those that are even more complex ones.  

Like every technology, artificial intelligence can outdate with time. As technology advances, previous benchmarks that defined artificial intelligence become outdated. For example, machines that calculate basic functions are no longer considered artificial intelligence since this function is now taken for granted as a simple computer function.  

There are two categories of artificial intelligence: weak AI and strong AI. Weak artificial intelligence refers to a system designed to carry out one particular job. These systems include computers playing chess and personal assistants such as Siri. While strong artificial intelligence systems carry on the tasks considered to be human-like, more complex, and complicated procedures, such as self-driving cars. 

Machine Learning 

Machine learning is a subset of Artificial Intelligence. It refers to the concept that computer programs can automatically learn from and adapt to new data without being assisted by humans.  

Machine learning is beneficial to processing big data. Many companies are dealing with vast amounts of big data available in different formats. Companies realize tremendous insights that can be gained from tapping into big data, but there is a lack of the resources and time required to analyze it. So, one of the methods to process and take insights from big data is using Machine Learning.    

Two of the most widely adopted machine learning methods are supervised learning and unsupervised learning – but there are also other machine learning methods.  

Supervised learning means that the algorithm is trained using labeled examples, such as an input where the desired output is known. While unsupervised learning is used against data that has no historical labels. The system is not told the “right answer.” The algorithm must figure out what is being shown. 

AI in speech recognition process 

Automatic speech recognition refers to technologies built to process human speech and turn it into text. The first take at speech recognition dates back to 1952 when Three Bell Labs researchers created a system called “Audrey” for single-speaker digit recognition.  

Automated transcription usually relies on automatic-speech-recognition (ASR) machines, which are based on Artificial Intelligence. When talking about voice recognition, it is recommended to couple the technology with human editors so it will help to achieve faster results, ensure better accuracy and quality.  

Advanced versions of ASR technologies now incorporate what is known as Natural Language Processing (NLP). These capture real conversations between people and use machine intelligence to process them. The accuracy provided by ASR is dependent on many factors, including speaker volume, background noise, the recording equipment used, and more. 

How does Automatic Speech Recognition work: 

1. An individual or a group speaks, and an ASR software detects this speech.

2. The device then creates a wave file of the words it hears.

3. The wave file is cleaned to delete background noise and normalize the volume.

4. This filtered waveform is then broken down and analyzed in sequences.
    
5. The automatic speech recognition software analyzes these sequences and employs statistical probability to determine the whole words and then complete sentences.

6. Professional human transcribers check the ASR’s work and correct any errors to achieve greater accuracy. 

The future of AI 

Artificial intelligence is impacting the future of virtually every industry and every human being. It has acted as the primary driver of emerging technologies like big data, robotics, voice recognition, and IoT. It will continue to act as a technological innovator for the foreseeable future.