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An ADC translates the analog waves of your voice into digital data by sampling the sound. The higher the sampling and precision rates, the higher the quality. Cite This! Humans have only mastered this art after millions of years of evolution. A computer, no matter how fast and complex it can be, will certainly fail to understand and analyze the following aspects of speech recognition:.
Human to human conversation is full of expressions, anecdotes, and emotions. With computers, we have not yet hit the phase where we can code them to interact with users like other humans. It would be extremely interesting to see how engineers and scientists are able to induce something as natural and human as verbal communication into computers that run on direct commands and instructions. Voice Recognition means making a computer understand human speech.
It is done by converting human voice into text by using a microphone and a speech recognition software. The basic recognition of speech system is shown below:. When sound waves are fed into the computer, they need to be sampled first. Sampling refers to breaking down of the continuous voice signals into discrete, smaller samples- as small as a thousandth of a second. These smaller samples can be fed directly to a Recurrent Neural Network RNN which forms the engine of a speech recognition model.
But to get better and accurate results, pre-processing of sampled signals is done. Pre-processing is important as it decides the efficiency and performance of the speech recognition model. They are then pre-processed, which is breaking them into a group of data. Generally grouping of the sound wave is done within interval of time mostly for milliseconds. This whole process helps us convert sound waves into numbers bits that can be easily identified by a computer system.
Inspired by the functioning of human brain, scientists developed a bunch of algorithms that are capable of taking a huge set of data, and processing that it by drawing out patterns from it to give output. These are called Neural networks as they try to replicate how the neurons in a human brain operate. They learn by example.
Neural Networks have proved to be extremely efficient by applying deep learning to recognize patterns in images, texts and speech. Recurrent Neural networks RNN are the ones with memory that is capable of influencing the future outcomes.
So RNN reads each letter with the likelihood of predicting the next letter as well. RNN saves the previous predictions in its memory to accurately make the future predictions of the spoken words. Using RNN over traditional neural networks in preferred because the traditional neural networks work by assuming that there is no dependence of input on the output. They do no use the memory of words used before to predict the upcoming word or portion of that word in a spoken sentence.
So RNN not only enhances the efficiency of speech recognition model but also gives better results. This only takes a minute and simply involves reading a short text of a few lines. However, not all most recognition software uses enrolment but may require the user to say if they have an accent and to choose which one. When talking, people often hesitate, mumble or slur their words.
One of the key skills in using voice recognition software is learning how to talk clearly so that the computer or device can recognise what is being said. It can help to plan what to say and then to speak in complete phrases or sentences. Voice recognition software can misunderstand some of the words you speak and may put in similar-sounding words, so it can be important to proofread carefully.
While voice recognition software is improving all the time, the error rate can still be quite high. If corrections are made using voice recognition software either by voice or by typing, it can adapt and learn so that, hopefully, the same mistake will not occur again. It can be possible to achieve very high levels of accuracy with careful dictation and correction, and perseverance. The text-to-speech facility is especially useful for people with a sight impairment who would find it difficult or impossible to read any text file and for anyone with dyslexia.
Training is really useful for users to realise the full benefits of working with voice recognition programmes. To get the best from training, it can be helpful to spread it out over a period of weeks — giving the user sufficient opportunity to practice new skills and consolidate their learning between formal coaching sessions.
Training will be most effective when it is geared towards the specific needs of the individual, focusing on their particular tasks and challenges. Specialist vocabularies can be attained by using plugins or by giving the programme access to emails and documents.
A wide range of private and voluntary organisations offer computer training services. The AbilityNet factsheet on Technical help and training resources gives contact details for many organisations that provide ICT training and support for disabled people. Apple provides tutorials and guidance on setting dictation on the Mac. Windows provides tutorials for their voice recognition. Nuance provides extensive tutorials and support for their Dragon products.
These programmes are all moderately priced, with a free version of NaturalReader also being available. My Computer My Way is an AbilityNet run website packed with articles explaining how to use the accessibility features built into your computer, tablet or smartphone. The site is broken down into the following sections:.
Use it for free at mcmw. Many of our volunteers are former IT professionals who give their time to help older people and people with disabilities to use technology to achieve their goals. Our friendly volunteers can help with most major computer systems, laptops, tablet devices and smartphones.
View a copy of this license at creativecommons. My Computer My Way Vision - seeing the screen Hearing - hearing sound Motor - using a keyboard and mouse Cognitive - reading and spelling. Print this page. This factsheet provides an overview of how you can use voice recognition. You can use voice recognition to control a smart home, instruct a smart speaker, and command phones and tablets.
In addition, you can set reminders and interact hands-free with personal technologies. The most significant use is for the entry of text without using an on-screen or physical keyboard. Communication technology continues to evolve rapidly. Using voice recognition to input text, check how words are spelt and dictate messages has become very easy. Most on-screen keyboards have a microphone icon that allows users to switch from typing to voice recognition easily. For some disabled people who might struggle or find it impossible to work with a mouse or keyboard, speech recognition enables a world of productive possibilities.
It can free people from typing and keyboard use, helping those with physical impairments and reducing the risk of repetitive strain injury from excessive typing or mouse use.
For example, people with dyslexia can write more fluently, accurately and quickly using voice recognition and may find it less stressful than conventional handwriting or typing.
Contents include 1. Once that is done it can digitize the spoken words into text form for subsequent editing and final conversion This technology may seem very simple to operate, right?
Speech Recognition Machines Up until some time back, machines were not able to work properly in noisy environments. Final Thoughts Today, we are seeing an increasingly large number of healthcare organizations shifting to this revolutionary technology to streamline the workload of busy clinicians.
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