Syntax and semantics play a big role in how a computer is able to recognize speech.

Syntax

There are three categories of syntax - tokenization, stemming and lemmatization. Tokenization can be divided into two more categories: sentence tokenization and word tokenization. Sentence tokenization refers to the division of a paragraph into individual sentences, while word tokenization refers to the division of a sentence into individual words. This allows the computer to recognize the potential meaning and purpose of each unique word.

Base refers to the process of reducing a word to its base or root. It does this by cutting off universal prefixes and suffixes. Stemming is a powerful method, but it is based solely on common prefixes and suffixes. Sometimes it cuts out the necessary components and changes the meaning of the original word.

Lemmatization helps to solve this problem. Instead of splitting the beginning and the end, lemmatization reduces a word to its root form by analyzing the word morphologically.

Semantics

Semantics can be broadly divided into the recognition of named entities and the creation of natural language. Named entity recognition allows a computer to classify certain words in a sentence. This is useful for a word such as "Google", which can be designated as an organization or a verb. Natural language generation is the process by which a machine creates natural human language. It uses mathematical formulas and numerical information to extract patterns and data from a given database and output understandable human language text.

What is text-to-speech?

Text-to-speech software converts written text to speech. There are hundreds of online text-to-speech applications and many free text-to-speech downloads are available. This technology offers a lot of benefits for people who prefer to listen to reading. It is also great for people who want to listen to a snippet of text while performing another task. For example, you can listen to audio from a book while driving.

Text-to-speech is also one of the ways that visually impaired people use content. It is known to improve literacy skills, reading comprehension, accuracy and the ability to recall information. Text-to-speech can improve word recognition skills and pronunciation abilities.

Text-to-speech conversion should not be confused with speech-to-text conversion. The latter refers to speech recognition, where the computer uses your voice input to execute commands. Another potentially confusing term is voice-to-text. What is voice-to-text? Simply put, speech-to-text conversion can also be referred to as voice-to-text.

Benefits of speech recognition and speech-to-text conversion

Speech recognition technology, such as at gglot.com, has transformed communication and reduced the time it takes to complete tasks. It has also enabled people to use technology in ways that were not possible before. Listed below are some of the benefits of speech recognition and the benefits of voice recognition.

Assistance to people with disabilities

The most obvious benefit is that speech recognition technology has made it possible for people with disabilities to type and operate computers using their voice as input. Prior to this technology, many people with certain physical disabilities could not use computers effectively, if at all. Speech recognition has brought these people justice and allowed them to participate in our high-tech society.

Writing

Using advanced algorithms and natural language processing, voice recognition applications ensure that we always use the correct spelling and use words appropriately. Voice recognition has made it possible to write accurately and legibly. It saves a lot of time, especially in the workplace.

Increased speed

For people who can't type or who type slowly, voice recognition is a game changer. Also, long hours spent typing are known to cause musculoskeletal disorders. Thus, voice recognition is a safer and faster alternative to putting your thoughts into print.

Specialization

Speech recognition will also allow many industries to change. For example, in the medical field, doctors can now add medical notes directly to patient files without having to type or write anything down. They simply speak into a voice recorder and the patient's electronic medical record is automatically updated. This has allowed doctors to spend more time treating and saving lives.

Speech recognition history

In 1791, Wolfgang von Kempelen built the first acoustic mechanical speech machine. It consisted of a mech, a reed and a synthetic rubber neck. With an experienced user, this machine could produce complete sentences in English, French and Italian.

After Kempelin's device, there was a lull in invention for more than a century, until a device known as the Radio Rex was invented in 1922. It was the first machine capable of speech recognition. It was a toy dog controlled by a spring and mounted on an electromagnet. The electromagnet was interrupted when an acoustic signal of 500 hertz appeared.

In 1952, Bell Labs built the Audrey system. This device could only recognize ten digits.

Then came the IBM Shoebox - released in 1962 - which could understand 16 words, numbers from 0 to 9 and perform mathematical calculations. This early computer was developed 20 years before the IBM personal computer was developed in 1981.

In 1962, the IBM 704 was invented. It was the first machine that could sing a song - "Daisy Bell".

By the 1970s, machines could recognize about 1,000 words. One such tool, called Harpy, was developed at Carnegie Mellon University in Pennsylvania with support from the US Department of Defense.

About ten years later, the same group of scientists developed a system that could analyze not only individual words, but entire word sequences. Among the first virtual assistants to apply this technology were automated assistants - the standard automated voices we still hear when we dial a customer service number.

In the 1990s, digital speech recognition was a new feature of personal computers and companies such as Microsoft, IBM, Philips, Lernout and Hauspie vied for customers. The latter was a Belgian speech recognition company that went bankrupt in 2001.

The release of the first IBM Simon smartphone in 1994 laid the foundation for the smart virtual assistants as we know them today. The history of voice recognition, when computers could recognize certain speakers, can be traced back to this point.

Siri on iPhone 4S was the first modern incarnation. Google Now came a year after Siri, providing voice recognition in Android. Today, voice assistants are everywhere.