It has been man’s undying quest to create his own that would think and act like him and after centuries of work AI was born. As machines got smarter humans got tired of typing out commands and thus “Speech recognition” was invented and things have never been the same since…
Early speech recognition systems had challenges in identifying accents, tones and other nuances that is a part of natural human language not to speak about the thousand of different languages that humans use to communicate with each other. As the technology evolved and the intelligence component in them grew by leaps and bounds and speech recognition started becoming personalized. Today your mobile and other personal technology is programmed to recognize your voice, understand your requirement and respond intelligently.
AI for speech recognition has two primary facets, understanding the human thought process and expresses this understanding through appropriate responses systems from the machine or technology in use.
People simply speak differently and the challenge in building a system that recognizes all voices is almost impossible. Personalization increases the effectiveness and dependability of the voice recognition system, therefore the computer needs to be trained to recognize and understand a particular voice. The technology needs to learn the uniqueness and different characteristics of the user’s language and voice. So users first prepare the software by speaking to it, let it analyze the speech and progressively respond to the commands.
There are instances when personalization is not required and a generic voice recognition system is in place. This has its limitations and is less accurate and less dependable.
Training a machine to recognize a particular voice is tedious and time consuming, but the resultant outcome is rewarding. It is not just enough to match the voice, the technology needs to think and respond. This thinking is dependent on pre-fed models which help them understand patterns, analyze them and create appropriate responses. Machines are now not only equipped with pre-fed models but are built with the capability to create its own new models. With the advent of Deep Learning and neural networks the speech recognition is set to conquer newer horizons.
Simple voice commands are used to initiate phone calls, select radio stations or play music from a compatible Smartphone, MP3 player or music-loaded flash drive.
The medical transcriptions/ Documentation industry is extensively using speech recognition technology to document prescriptions, diagnosis and treatments and reports, case studies and create reference material.
Applications typically include setting radio frequencies, commanding an autopilot system, setting steer-point coordinates and weapons release parameters, and controlling flight display.
People with Disabilities:
Speech recognition is also very useful for people who have difficulty using their hands due to injuries or permanent disabilities. Speech recognition is used in deaf telephony, such as voicemail to text.
The Siri’s , Alexa’s have advanced speech recognition systems and every computer or mobile these days come with built in speech recognition capabilities.
Other applications include Home automation, Education, Robotics and more, the list is long and each day newer capabilities and applications are being added to this technology. The day is not far away when Voice recognition systems will dominate the way we communicate with our machines.