Share the latest technology trends or photos of gadgets you love.

How Does Speech Recognition Work?

How Does Speech Recognition Work? This Will Leave You Amazed

Speech recognition systems are efficient means of reducing manual work and saving time in today's fast-paced life. The software that we use today are highly developed and detect words that are spoken at a normal rate, without any pause.
Shashank Nakate
Last Updated: Jan 11, 2019
The process of converting spoken words in a form that is machine readable is termed as speech recognition. This concept has found many applications in today's tech-savvy world, and is used in various fields like health care, military, business, legal transcription, etc.
On the basis of vocabulary and the number of users, these systems are categorized into small vocabulary/many-users, and large vocabulary/limited-users, or discrete and continuous speech recognition. In discrete systems, the dictator has to pause after every word spoken. Continuous systems understand words that are spoken in a normal manner.
Working of Speech Recognition Systems
When a person speaks, vibrations are created. Technology converts these vibrations, i.e., analog signals into a digital form by means of an analog-to-digital converter (ADC).
Digitization of sound takes place by measurement at regular intervals. The sound is filtered into different frequency bands and normalized, so that it attains a constant volume level. It is also checked if the sound matches stored templates.
The next step is dividing analog signals into segments that range from a few hundredths to thousands of a second. These segments are matched with phonemes that are already stored in the system. Phonemes are specific sounds that are understood by people speaking a particular language.
Statistical modeling systems, which use mathematics and probability, play an important role in today's speech recognition systems. These systems are used to determine or predict the outcome after a particular phoneme.
It becomes easier to predict where a particular word begins and ends. The Hidden Markov Model and Neural Networks are two statistical modeling systems, out of which the former is more common.
The outcome after a particular word in a sentence depends upon the vocabulary. It is difficult even for a computer to determine the possible outcome after a particular phoneme, due to the sheer number of words in a language.
Thus, it is necessary to 'train' the system. Speaking into the system helps in the training process. Once the user gets used to the system, it becomes easy for the system to determine the possible outcome after a particular word or a phoneme.
It is important to familiarize the speech recognition system with the user's voice to get better results. However, one shouldn't expect 100% accuracy from these technologies, because the tones and the way one pronounces a particular word, changes from region to region.
However, speech recognition technology still remains a useful invention and caters to the needs of many people, including individuals with disabilities, and such technology can only improve with time.