Know everything about “Voice Recognition Based On Artificial Intelligence.” Voice recognition transforms an acoustic signal captured by a telephone or a microphone into a set of spoken words. It is computer software or a hardware device that can decode the human voice. The said words can be used as commands and control, data preparation, and data entry. They can also serve as input for further verbal processing to execute speech understanding. Voice recognition is commonly used to operate a device, perform commands, or write something without the need to press any buttons on the keyboard or use a mouse. Let’s know more about Voice Recognition Based On Artificial Intelligence.
In recent times voice recognition has been performed with the help of Automatic Speech Recognition (ASR) software. The software’s utilization of ASR needs the user to train the ASR to identify the user’s voice to convert the speech to text more accurately. For example, the user could say “open maps,” and the computer should open “Google maps.”
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History of Voice Recognition
The first speech recognition systems were not focused on words, but they were made to focus on numbers. In 1952, Bell Laboratories designed the Audrey system to identify a sole voice speaking the numbers aloud. After ten years, IBM launched Shoebox that recognized and would answer a total of 16 words in the English language.
Throughout the globe, other countries developed hardware that could understand sound and speech. And by the end of the ’60s era, the technology could recognize words with four vowels and nine consonants.
During the 1970s, Speech recognition made several significant progress and changes in this decade. The main reason behind this change was the US Department of Defence and DARPA. They ran the Speech Understanding Research (SUR) program, and it was one of the essential kinds within the history of speech recognition. From this program, Carnegie Mellon’s Harpy speech came, capable of recognizing more than 1,000 words roughly similar to that of a three-year-old’s vocabulary. Also, a remarkable thing within the ’70s was Bell Laboratories’ establishment of a system that would interpret various voices.
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With the time technology grew and in 2011, Apple set in motion Siri like Google’s Voice Search. The first part of this decade (2011 – 2020) saw an explosion of other voice recognition apps. Amazon’s Alexa, Google Home, has seen people getting more and more accessible reproof machines.
Nowadays, various of the biggest tech companies compete to be the simplest in the voice recognition system. In 2016, IBM pulled off a word error rate of 6.9%. In 2017 Microsoft usurped IBM with a 5.9% claim. Shortly at that time, IBM improved its rate to 5.5%. However, it’s Google that’s claiming the shortest rate at 4.9%.
How to Achieve Speech Processing through Neural Networks
An Artificial Neural Network is a computer program that attempts to duplicate the biological elements of the human brain. Artificial Neural networks are an excellent grouping system, and they have been effective with noisy, patterned, variable data streams containing various overlapping and incomplete cues.
Neural networks do not need the complete specification of a problem. They keep on learning through exposure to vast amounts of example data. Thye comprises an input layer, one or more hidden layers, and one output layer. How they’re organized is called a network of architecture.
Advantages of Voice RecognitionBased On Artificial Intelligence
- Helps to solve inefficiencies and helps in time management.
- In medical centers, doctors and nursing staff don’t have to use a keyboard anymore.
- The software in time will learn to recognize a specific doctor’s distinctive speech pattern.
- The software will spell every word with the utmost accuracy.
- Doctors can produce a large number of notes that they can later edit.
- Doctors can write medical papers without being grasp back by typo errors on the keyboard.
- The use of voice recognition is growing very quickly. Windows and Macintosh OS both make the use of voice recognition and they are already built-in.
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Disadvantages of Voice Recognition Based On Artificial Intelligence
- In the early days, the software requires to be train to identify the user’s voice. This recognition can be accomplished by reading some passages provided by the user.
- The doctors required to speak clearly for the software to work efficiently.
- The software used to take more time to complete the training, if any doctors have trouble speaking properly, such as tends to run words together, mumble some words, etc.
- The software only spells the words it can perceive from the training.
- Sometimes it can recognize 5-20% of words incorrectly which can promote problems for the user.
- While the doctors may talk at a lot of speedier movement, the words they say can be disruptive or grammatically incorrect.
- Doctors and the staff may need to alter in some cases.
- Voice recognition software utilizes a lot of memory and they have explicit hardware necessities.
- It’ll make you greater time to edit and review the mistakes.
- Voice recognition software may have a problem with accents. The user may have to talk consistently and clearly at all times to limit the mistakes.
- You’ll require a quiet environment all the time as the background noises may interfere with the quality of your voice.
- Talking for long times can cause some roughness, dry mouth, and minor to significant vocal issues.
Types of Neural Networks
Taking motivation from our biological nervous system, neural networks structure a severe structure to data handling. Compared with ordinary computers, neural networks take very different strategies for problem-solving. There are a total of 6 different types of neural networks:
- Feedforward Neural Network
- Radial Basis Function Neural Network
- Kohonen Self- Organising Neural Network
- Recurrent Neural Network
- Modular Neural Network
- Physical Neural Network
Which Type of Neural Network is best for Voice Recognition?
Recurrent Neural Network (RNN) is the best type of neural network for voice recognition. The output of a given input is not only based on the information at a given time but also on the inputs that came before. It brings in a new bunch of problems. Long-Short-Term-Model attempts to solve this by introducing particular nodes that blend new input with information from the past.
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