The science of speech recognition has come a long way since 1962. The technology has developed, speech recognition has become progressively implanted in our everyday lives with voice-driven apps like Amazon’s Alexa, Apple’s Siri, Microsoft’s Cortana, or the many voice-responsive features of Google. From our phones, computers, watches, and even our refrigerators, each new voice-interactive device that we bring into our daily lives extends our need for artificial intelligence (AI) and machine learning.

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Role of Arti cial Intelligence and Machine Learning
in Speech Recognition
venkat k
Oct 9, 2019 · 3 min read
The science of speech recognition has come a long way since 1962. The technology has
developed, speech recognition has become progressively implanted in our everyday lives
with voice-driven apps like Amazon’s Alexa, Apple’s Siri, Microsoft’s Cortana, or the
many voice-responsive features of Google. From our phones, computers, watches, and
even our refrigerators, each new voice-interactive device that we bring into our daily
lives extends our need for artificial intelligence (AI) and machine learning.
Artificial Intelligence and Machine Learning:

2. 5/14/2020 Role of Artificial Intelligence and Machine Learning in Speech Recognition
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For the first time, Ai services could be defined as human intelligence displayed by
machines. Where it was first used to analyze and quickly compute data, artificial
intelligence now allows computers to do things that only humans can do.
Machine learning, a subset of artificial intelligence, refers to systems that can learn by
itself. It teaches a computer to identify patterns, not programming with specific rules.
Giving large amounts of data to the algorithm during the training process and allowing
it to learn from that data and identify patterns. In the early days, programmers had to
write code for every object they wanted to identify. Now a system can identify both by
showing several examples of each. As a result, these systems continue to evolve over
time without human intervention.
There are many different approaches and approaches to machine learning. One of those
approaches is artificial neural networks, for example, product recommendations. E-
commerce companies often use artificial neural networks to show you which products
you buy. They can do this by taking data from their customers’ browsing experiences
and using that information to make effective product recommendations.
Some other common applications of Artificial Intelligence are object recognition,
translation, speech recognition, and natural language processing. ASR is the conversion
of the spoken word into text, but NLP is the processing of text to get its meaning. Since
humans often speak in conversations, abbreviations, and acronyms, extensive computer
analysis of the natural language is essential to produce accurate transcription.
Also Read: How To Make AI Voice Assistant Apps For Android?
Experiments With Speech Recognition Technology.
There are a lot of experiments with speech recognition technology but they are
constricted. They contain week recording equipment, background sound, tough tunes,
and various pitches of voices.
Teaching the machine to communicate language similar to humans, which is not yet
impeccable. Attending and accepting what a person says is much more than listening to
the words a person voices. As human beings, we read the person’s eyes, their facial
gestures, body movements, and even the voices and modulations in their voice. Another
shade of speech is the human tendency to reduce certain words. We have been saying

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short words for a long time and we positively do not pronounce when we learn them.
This human nature fakes another task to machine learning in speech recognition.
Machines are learning to “hear” voices, emotions, and inflections, but there are still
many ways to go. As technology gets more classy and more and more data is used by
particular algorithms, those tasks are quickly overcome.
The technology that supports voice-powered interfaces is very powerful. Advancement
in Artificial Intelligence and easy-to-use speech data for machine learning purposes, it is
not surprising if this becomes the next dominant user interface.
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