Yesterday’s blog discussed the developmental history of natural language processing (NLP) technology. From the 1950s to today there have been various developments in the field of NLP that have made it a critical component of speech recognition technology and have enabled application development for myriad devices.
Speech recognition is only one of the functionalities that use NLP. Other crucial tasks that NLP performs include automatic summarization (production of a readable summary of a text or a portion of a text), machine translation (the automatic translation of text from one language to another) and question answering (the capability of answering human-language questions).
Speech recognition is considered the opposite of text-to-speech, which is the process by which a computer system converts typed text into spoken words. Speech recognition is the conversion of spoken words. Voice recognition is featured in many interactive voice response applications and is helpful in enabling hands-free phone usage.
Often times an automatic speech recognition (ASR) functions more smoothly when the recognition system has been trained to detect the voice of a particular speaker. However, speech recognition also works for multiple speakers, as the application is designed and programmed to recognize speech in general, without being customized to a single speaker.
There are many ways in which ASP can be effectively used. It can be programmed into car or home audio systems in order to enable hands-free communication (voice dialing), appliance control (both issuing and processing verbal commands) and even data entry (users can submit credit card, billing or shipping information by simply reciting their info).
One practical use for NLP as documented by the Belgian technology consulting firm Nmahn is to conduct searches of both the Internet and computer databases. Many search engines rely on Boolean searches to increase their searches. Boolean searches are typically defined as ones that have tow data values that are true or false.
However, most search engines today are powered by NLP as opposed to Boolean, giving users what is traditionally thought of as a more friendly search engine experience.