Speech recognition is an emerging interdisciplinary subfield in computational linguistics and computer science, which develops tools and methodologies that enable computers to recognize, classify, and translate spoken language into words with high accuracy and high quality. It has opened up new possibilities for speech recognition technology as it is helpful in various fields such as telecommunication, finance, medical, and human resources. Multiple technologies are used in speech recognition. Some of these are content recognition, the association grammar, voice recognition, and neural network technologies.
The success of speech recognition depends on two things.
- The accuracy of the process of identifying and classifying the speech or the output produced.
- Secondly, the performance of the system needs to be good enough to deliver the correct output.
However, even though speech recognition technology’s accuracy is one of the most important factors, many think that it can be improved. The advancements made in speech recognition technology give a strong indication of what the future holds in store for this field.
The deep learning algorithm introduced by Google, which can deliver speech recognition results in time, is one of the major breakthroughs in speech recognition technology. This is also an essential tool that enables speech recognition software to be more accurate than ever before. The deep learning method is based on the idea of artificial intelligence. The concept of artificial intelligence is about the unsupervised training of systems, where computers can learn without any supervision. Deep learning uses the same idea, which makes deep learning much more accurate as of the tasks it is trained for are much larger than what simple software can handle.
Many software developers believe that even a small error in speech recognition can cost the company or business a lot of money, especially in customer service, sales, and marketing.
As a result, speech recognition must be accurately done to save both time and money. Google has made great strides in speech recognition accuracy. They have developed tools like Google Brain, which is extremely good at recognizing human voices, and this is another tool that they use to improve their accuracy.
Another tool used in speech recognition accuracy is called the Natural Language Processing (NLP) tool. It is an advanced form of software that can detect particular characteristics of a voice. This includes tone, pitch, intonation, register, and diction. NLP software is commonly used for voice recognition.
The next step up from NLP is the Deep Speech Recognition (DSR) tool. This speech recognition has an unbelievable accuracy rate that exceeds 95%. The NLP Deep Speech tool can recognize not only human voices but also all kinds of noises. It has recognized individual voices of call center agents, pilots, weather forecast generators, audio clips from television shows, telemarketers, and even the strains of babies’ voices. DSR also distinguishes between different accents of the person and corrects the mistake. It also can speak very fast and clearly.
With so much information bombarding our senses daily, we have become so dependent on computers in this day and age. And with the progress made in speech recognition software has made our lives so much easier. With just a few clicks of the mouse or a few words typed into a speech recognition program, the whole process of recognition becomes effortless. There is no more need to read through pages of transcript to understand what a person is trying to say.
One can get his image cropped or resized by speech recognition. Speech recognition makes it possible for someone to give a speech and not have the audience look at the computer screen. So, it will make an excellent video presentation. The medical industry has also started using speech recognition technology for their patients who are receiving respiratory treatment. The patients are being asked to talk into a device, and then the speech recognition will analyze the words they are saying. These results are fed back to the screen, and the next day the doctor can look over the file and immediately understand what is happening.
- Artificial Intelligence (AI)
- Computer Programming
- Content Management System (CMS)
- Customer Relationship Management (CRM)
- Deep Learning
- Internet of Things (IoT)
- Machine Learning
- Search Engine
- Software as a Service (SaaS)