What is Automatic Speech Recognition and Natural Language Processing?

Learn the artificial intelligence course in Delhi by Madrid Software Trainings. We might still be at least a few decades away from getting fully autonomous, intelligent artificial intelligence systems interacting with us in a genuinely 'human-like' way in terms of technological growth. But, in many ways, due to the ongoing advancement of what is known as automated speech recognition technology, we are moving steadily toward this future scenario at a remarkably fast pace. And at least so far, for all kinds of applications, it seeks to promise some genuinely useful user interface innovations.

Automatic Speech Recognition -

Speech recognition software is a rising part of our lives, but luckily not as the 90's sci-fi movies led us to believe. Whether it's Siri, Cortana, Amazon Home or Google Gnome or Petlexa on April Fool's Day, we chat more than ever with digital assistants. The first time that speech recognition technology was made accessible to the general public and the headaches that came with it, you can recall. But Google's word accuracy rate rose from 80 percent to an impressive 95 percent between 2013 and 2017. Speech recognition, also known as automatic speech recognition (ASR), machine speech recognition, or speech-to-text, is a capability that allows human speech to be processed in a written format by a program.

Although speech recognition is generally confused with voice recognition, speech recognition focuses on converting speech from a verbal format to a text one, whilst voice recognition attempts only to recognize the voice of an individual person.

How Speech Recognition works?

You use an audio feed to talk to the software. A wave file of your words is generated by the computer you're talking to. Removing background noise and normalizing volume cleans the wave file. The resulting shape of the filtered wave is then split into what are called phonemes. It can make an extremely accurate, informed guess as to what you are saying based on algorithms and previous feedback. It gets to know the use of language by the speaker. Unsurprisingly, if only one person uses the speech recognition program, it would be trained specifically for how that person speaks.

Directed Dialogue conversations are the much simplified version of ASR at work and consist of computer interfaces that tell you to answer verbally from a restricted list of choices with a specific phrase, thus shaping their response to your strictly specified request. Automated telephone banking and other client service interfaces often use ASR software for guided dialogue.

Natural Language Processing -

The most advanced variant of ASR technologies produced revolves around what is known as Natural Language Processing, or NLP in short. This ASR variant is the closest to enabling real communication between individuals and machine intelligence, and although it still has a long way to go before hitting an apex of development, we are already seeing some impressive results in the form of smart phone interfaces such as the iPhone Siri program and other systems used in business and advanced technology contexts.

There are 60 thousand or more words in the standard vocabulary of an NLP ASR system. Now, if you say only three words in a series, what this means is over 215 trillion potential word combinations! Obviously, for an NLP ASR method, scanning the entire vocabulary for each word and processing them individually will then be grossly impractical. Instead, what the natural language system is programmed to do is respond to a much smaller list of "tagged" keywords selected that provide longer requests with meaning. Thus, using these contextual cues, the machine can narrow down exactly what you're saying to it much more easily and figure out which words are being used so that it can respond appropriately.

In higher-level NLP skills, these underlying duties are also used, such as:

• Categorizations of content.
• Discovery of the topic and modeling.
• Extracting contextually.
• Analyzing sentiment.
• Conversion of speech-to-text and text-to-speech.
• Document summarization.
• Translation by computer.

Top NLP tools -

In artificial intelligence, natural language processing is one of the most dynamic areas. But it doesn't need to be too difficult to try your hand at NLP tasks like sentiment analysis or keyword extraction. There are several online NLP resources that make language analysis available to all, enabling you to very quickly and intuitively analyze large amounts of data. SaaS platforms are ideal alternatives to open-source libraries, as they offer ready-to-use solutions that are often simple to use and do not require knowledge of programming or machine learning. These tools are also useful for someone who doesn't want to invest in additional resources or time coding.

• IBM Watson
• MonkeyLearn
• Google Cloud NLP
• Lexalytics
• Amazon Comprehend
• MeaningCloud

Applications of NLP -

The analysis of natural languages Processing enables organizations to make sense of all kinds of unstructured data, such as emails, social media messages, product reviews, online polls, and tickets for customer service, and to gain useful insights to strengthen their decision-making processes. NLP is often used by businesses to automate repetitive tasks, reduce time and expense, and gradually become more effective.

In the healthcare sector, NLP is especially booming. As healthcare facilities are rapidly implementing electronic health records, this technology is improving patient quality, disease detection and reducing costs. The fact that clinical documentation can be enhanced indicates that better healthcare can be used to better understand and support patients. The aim should be to optimize their experience, and this is already being focused on by many organizations.

Conclusion -

Businesses simplify some of their everyday processes and make the most of their unstructured knowledge thanks to NLP, gaining actionable insights that they can use to maximize customer loyalty and provide improved customer experiences. Many different strategies for decoding human language include natural language processing, varying from statistical and machine learning methods to rules-based and algorithmic strategies.

Amit is the Digital Marketing head at Madrid Software Trainings. Madrid Software Trainings is the fastest growing Ed-Tech company in India.Madrid Software Trainings