How Artificial Intelligence Assign Better Meaning with Voice NLP?

Natural language processing is an Artificial Intelligence-powered means by which computers dissect the words you input in either text or voice and process your request. This has benefited from years of training and advancements in the algorithms powering it.

Voice NLP refer to any process by which a machine analyzes and extracts meaning from a human speech that varies in scales of complexity. An NLP algorithm uses to evaluate a large data file to extract specific terms and phrases. Complex NLP algorithm existed in Siri and Alexa, parsing user input, converting it to text, analyzing that text, providing an answer, and converting it back into an audio response.

It is innocuous as Google search results use NLP algorithms to interpret
your search’s meaning rather than mining-specific products that match the
keywords provided. AI allows moving beyond user-input to user-intent,
extracting a meaning behind shortened phrases and colloquialisms to fully
understand the importance of a query and provide a more specific answer.

How Natural
Language Processing Work?

Computer languages follow a rigid protocol. If you miss an operator or
an argument, it might break completely. Human language has no such
restrictions. Simultaneously, somebody’s speech might become significantly less
precise, which are incredibly flexible for enabling expressions. Historically,
this made them difficult to capture and analyze with machines.

Deep Learning changed by enabling AI systems to evaluate and parse human language with an incredible degree of accuracy. This is called as NLP.

For decades, scientists are writing rules to parse grammar and spelling
to extract meaning from text. But it wasn’t until shifted to a machine learning
model that progress became readily apparent which took many years to reach a
point of complete, accurate language analysis to the degree that it could
become interchangeable.

For example, Google Translate, completely revamped using Google’s
TensorFlow framework introduced a neural machine translation instead of an
earlier statistical model. The result acts a highly accurate translation tool
for most common language pairings.

NLP has since used various additional tasks, including language
generation, entity recognition, speech tagging, sentiment analysis, and speech
recognition.

Speech recognition is around some time before the advancements in NLP. The software was long able to capture speech in text-form, with lesser accuracy. NLP is different as it goes beyond capturing addresses. It synthesizes, analyzes, and assigns meanings to speech enabling developers using that data in new forms as

  • Translating
  • Consolidating
  • Searching for
    correct data.

Applications of NLP

NLP is utilized in dozens of industries to streamline operations and
address long-standing pain points. More than ninety-five hospitals using
electronic health records and the amount of time spent on documentation to meet
new regulations have boosted throughout the industry. This burnout creates limitation
in the time practitioners can spend with their patients and reduces their
efficacy. NLP is used to process those large volumes of data faster and with
less practitioner input. It is effectively used to extract data and clinical
information into a format that can be more readily accessed and consumed by
doctors.

NLP is being used in several industries to streamline communication with
customers. Chatbots have become ubiquitous, but have been incredibly impactful
in high-touch sectors like finance. They enable customers to access information
that required a human agent. Essential questions are asked and get routed to
the correct agent when a problem is too complicated.

These applications for NLP includes in three forms:

1. Sentiment Analysis

Data is inherently objective, and precursors of modern NLP relied
heavily on quantitative input to extract meaning. NLP algorithms evaluate
things like social media posts, market research panels, questionnaires, and interviews
to identify common elements of crucial emotions and get a general impression of
someone’s sentiment. For example, Cognovi Labs has used such an algorithm to
build their Coronavirus Panic Index, measuring how people react to the pandemic
in real-time.

2. Data
Summarization

A total of human knowledge present and the volume of information is
overwhelming. NLP reduces long texts to high-level summaries that are more
actionable in business, academic research, and more.

3. Market
Intelligence

Increasingly, companies rely on NLP algorithms for identifying market
response to their products. Data come from website traffic, user reviews,
social media posts, customer service calls, and even offline sales stats. All
of which is effectively analyzed for an actionable pattern.

NLP applications are expanding rapidly as new-age NLP enables developers
to skip the text-to-speech step in processing input.

Behavioural analysis of speech data distinguishes vocal cues like pitch
and tonal variance, makes richest data than a raw text, and transcriptions
audio files as NLP operated traditionally.

Instead of strictly extracting meanings from the text, these newer NLP
algorithms are accurate for assigning intent and sentiment of a word.

Language Agnostic
Machine

One of the biggest challenges for business and computing is a language
barrier. There are more than seven thousand languages in the world, and
hundreds used in international trade. Some countries have dozens of dialects in
their own, creating significant barriers for computing applications that rely
on a handful of core languages.

Traditionally, expanding an NLP model to a new language, a new data set
must be recorded and annotated. Its a costly and time-consuming process that
does not scale in the right manner. However, profound learning advancements
allow developers to create cross-lingual embedding and systems like BART that
help to expedite new NLP models.

Future

Our lives interconnected with billions of chatbots and voice assistants
respond in language with a fraction of a second. While this has become
something of second nature for most consumers, the technology behind those
rapid responses is relatively new and has evolved quickly in recent years. It
holds a lot of scope and its applications are limitless.

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  • 29 December, 2020
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