Natural language processing :

Natural language processing which has roots in the field of linguistic is the ability of a computer program to understand and analyze human language as it is spoken and written. NLP is the core of various techniques and tools we use every day such as translation software, chatbots, spam filters, and search engines. NLP analyzes several aspects of natural human language like syntax, semantics, pragmatics, and morphology. Then the data is transformed into machine learning algorithms that can solve specific problems and perform desired tasks.NLP is defined as a problem maker to computers since it’s hard for them to master in understanding the concepts and meaning of plainly spoken human language. Conceptually, it’s a fairly straightforward technology.

“Nat­ur­al Lan­guage Pro­cessing is a field that cov­ers com­puter un­der­stand­ing and ma­nip­u­la­tion of hu­man lan­guage, and it’s ripe with pos­sib­il­it­ies for news­gath­er­ing, You usu­ally hear about it in the con­text of ana­lyz­ing large pools of legis­la­tion or other doc­u­ment sets, at­tempt­ing to dis­cov­er pat­terns or root out cor­rup­tion.”Some of the basic NLP preprocessing tasks are Encoding, marking part of speech, Derivation, Remove redundant words. Once the data is processed, it’s time to build an NLP algorithm.

Speech recognition is the most important application of NLP. It uses natural language processing to convert spoken language into a machine-readable format. Speech recognition is used in virtual assistants like Alexa, transcribing calls, machine translation, Sentiment analysis, keyword detection, text extraction, etc.

Machine translation involves translating text or spoken words from one language to another. The application can be used to reach a wide potential audience and understand foreign documents. Sentiment analysis or opinion mining is to determine whether data is favorable, pessimistic, or neutral. NLP application is also extended to spam detection, topic labeling, and intent detection.

NLP applications deliver a seamless path to interact with machines. Today, we have programs that assess and synthesize text and speech in unprecedented ways.

Speech recognition:

As previously mentioned, Speech recognition is one of the most popular applications of NLP. This procedure functions as a channel that transforms pulse code modulation digital audio from a sound card into recognized speech. Speech recognition technology has been massively used in every industry.

In speech recognition, the computer accepts sound vibrations as input using an analog to digital converter. Various algorithms run on the data to recognize the speech and convert it into the result. The result depends on the goals, for example, Google voice typing converts words to text format while virtual assistants like Alexa take speech input and gives voice feedback in return.

Speech recognition technology has multiple popular applications that enable the user to perform a wide range of voice-activated tasks.

Let’s go through some of the popular applications :

• Virtual assistants use speech recognition software to accept spoken input and deliver results as voice answers or in another form like playing music, news, searching, place an order, and so on.
• Armies or militaries all over the world are researching with speech to text conversion to make the job easy for pilots at the cockpit.
• Clinicians and doctors update patient’s medical records in real-time via voice notes.
• Speech recognition is capable to impact transportation services and streamline scheduling.
• Companies such as PayPal and Venmo allow customers to perform bank transactions through voice assistants.

Numerous industries are actively investing in speech recognition technologies like marketing, tourism, Health care, etc.NLP and speech recognition are two major subfields of artificial intelligence. Ai is continuously evolving to benefit various industries. Artificial intelligence forms the basis for all computer learning and is the future of all complex decision-making. AI is for today and also for a better tomorrow.

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