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Free Speech Recognition in AI Course

Speech Recognition in AI

star 4.61  Beginner level 1.5 learning hrs 2.8K+ Learners

Designed for Speech Recognition in AI beginners, this free course explains datasets, metrics, and analysis choices with examples that connect learning to practical decisions.

Instructor:

Vikesh Pandey

Speech Recognition in AI

star 4.61  Beginner level 1.5 learning hrs 2.8K+ Learners
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Skills you will gain

AWS

Speech Recognition

ASR

Key Highlights

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Master in-demand skills & tools

Test your skills with quizzes

About this course

A beginner-focused free speech recognition in ai course can reduce confusion by connecting ideas with situations learners actually recognize. The course introduces core concepts, system behavior, use cases, and risks in a way that shows how the pieces work together. Instead of treating speech recognition in ai as a list of terms, it explains what to look for, how to reason through common tasks, and why the subject appears in real projects, teams, or business decisions.


The course is a good fit if you want to understand how the technology works, where it is used, and what to check before applying it. You can use it to prepare for assignments, interviews, workplace conversations, or a first hands-on project depending on your goal. After finishing, you should be able to explain the core idea of speech recognition in ai, recognize when it is relevant, and choose a sensible next step such as practice exercises, deeper tools, related frameworks, or a more advanced course.

Stand out with an industry-recognized certificate

Start learning

10,000+ certificates claimed, get yours today!

Get noticed by top recruiters

Share on professional channels

Globally recognised

Land your dream job

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Course outline

What is speech recognition?

ASR Systems

Acoustic Modelling

Acoustic terms used in ASR

Evolution of speech recognition

General and Technical challenges with speech recognition

Hidden Markov Model

Search Space Decoder

Get access to the complete curriculum once you enroll in the course

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Speech Recognition in AI

rating icon 4.61

1.5 Hours

Beginner

Sign up with Email

2.8K+ learners enrolled so far

Get free course content

Master in-demand skills & tools

Test your skills with quizzes

Trusted by 1 Crore+ Learners globally

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Learner reviews of the Free Courses

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Reviewer Profile
Likith VK

5.0

★★★★ ★
India
“An Extraordinary and Informative Course”
I thoroughly enjoyed this course and found it to be highly informative and engaging. The curriculum was well-structured, covering a wide range of topics in depth. The instructors were knowledgeable and presented the material in a clear and concise manner. The quizzes and assignments were challenging yet fair, helping to reinforce the concepts learned. Overall, this course provided a comprehensive learning experience that was both enjoyable and educational.
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Reviewer Profile
Manish P

5.0

★★★★ ★
India
“An Engaging and Informative Course Experience”
I thoroughly enjoyed this course and found it to be highly informative and engaging. The curriculum was well-structured, covering a wide range of topics in depth. The instructors were knowledgeable and presented the material in a clear and concise manner. The quizzes and assignments were challenging yet fair, helping to reinforce the concepts learned. Overall, this course provided a comprehensive learning experience that was both enjoyable and educational.
Read More
Reviewer Profile
Vaishnavi Kondaparthy

4.0

★★★ ★ ☆
India
“Introduction to Speech Recognition: A Comprehensive Course”
This course introduces the fundamental concepts of speech recognition, including STFT, Mel Scale, LPC coefficients, and filter bank methods. It provides a strong foundation in speech signal processing techniques and their real-world applications. Perfect for beginners to enhance their knowledge and build a career in speech technology.
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Reviewer Profile
Aishwarya Shetty

5.0

★★★★ ★
India
“Enrolling in the speech processing course on GREAT LEARNING ”
was a transformative experience that deepened my understanding of how machines interpret and process human speech. The course covered foundational topics such as acoustic signal processing, feature extraction (e.g., MFCCs), and phoneme recognition, alongside advanced concepts like Hidden Markov Models (HMMs), Deep Neural Networks (DNNs), and speech synthesis techniques.
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Reviewer Profile
Neeha Velagapudi

5.0

★★★★ ★
India
“Introduction to speech recognition”
Speech recognition is the process of converting spoken language into text. It involves several stages of processing to identify, interpret, and transcribe spoken words into written form. Here's an overview: Key Components of Speech Recognition: Acoustic Signal: This is the sound wave produced when someone speaks. Important features of the speech signal (like phonemes, syllables, and sounds) are extracted. Techniques like Mel-Frequency Cepstral Coefficients (MFCCs) are used to represent speech in a form that is easier for machines to process.
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Reviewer Profile
Biyyani Indhupriya

5.0

★★★★ ★
India
“Speech recognition is a technology that enables a machine or computer to recognize and process human speech into a digital format, typically text.”
It involves analyzing and interpreting the acoustic signal (sound waves of speech) to identify spoken words, phrases, or commands. Speech recognition is used in various applications, such as: Voice assistants (e.g., Siri, Alexa, Google Assistant) Dictation software (e.g., Dragon NaturallySpeaking) Call center automation Language translation tools Accessibility tools for individuals with disabilities The key components of a speech recognition system include: Acoustic Model: Represents the relationship between audio signals and phonemes (basic speech units).
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Reviewer Profile
Mittapelly Shruthi

4.0

★★★ ★ ☆
India
“A meaningful learning experience is characterized by skill development, hands-on practice, and personal growth. It provides opportunities to apply theoretical ”
Speech recognition is a technology that converts spoken language into text by analyzing and processing audio signals. It works by capturing the acoustic signal of speech, extracting key features, and using models like the acoustic model and language model to interpret the sounds and predict the most likely words or phrases. The acoustic model identifies the relationship between speech sounds and their phonetic representations, while the language model ensures the output is linguistically meaningful. These components are combined by a decoder, which produces the final text .
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Reviewer Profile
Sivasankari Venkatraman

5.0

★★★★ ★
India
“The course was very insightful and gave a clarity about speech recognition and models”
The HMM model was explained clearly and the process of ASR was excellently explained
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Our course instructor

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Vikesh Pandey

Generative AI/ML Lead

Artificial Intelligence Expert

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5.9K+ Learners
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3 Courses
Vikesh is a seasoned Generative AI/ML professional with over 15 years of expertise in machine learning, cloud, and application development. He collaborates with some of the world’s largest financial institutions, designing and implementing generative AI/ML and MLOps platforms that scale seamlessly to thousands of users, while ensuring strict compliance with security, privacy, and regulatory standards. With a strong software engineering background, Vikesh has witnessed the evolution of technology—from desktop applications to monolithic web apps, n-tier architectures, SOA, and microservices—leveraging these experiences to drive innovation in machine learning solutions.

Frequently Asked Questions

Will I receive a certificate upon completing this free course?

Yes, upon successful completion of the course and payment of the certificate fee, you will receive a completion certificate that you can add to your resume.

Is this course free?

Yes, you may enroll in the course and access the course content for free. However, if you wish to obtain a certificate upon completion, a non-refundable fee is applicable.

What comes after learning Speech Recognition in AI online?

Move into a project, advanced course, or adjacent skill that uses datasets and metrics. The free course helps you choose that next step more clearly. It also keeps the learning focused on practical next steps.

How does Speech Recognition in AI connect with real work?

Real work often requires understanding purpose, limits, and tradeoffs. This course shows how datasets and metrics fit those decisions. It also keeps the learning focused on practical next steps.

Speech Recognition in AI: what comparisons make the subject clearer?

Compare the purpose, inputs, outputs, and limits of each idea. That habit helps you understand Speech Recognition in AI as a working skill, not just a topic name. It also keeps the learning focused on practical next.

In Speech Recognition in AI, what should beginners understand first?

Begin with datasets, metrics, and the purpose behind the topic. This free course gives you that starting point before you move into harder examples or tools. It also keeps the learning focused on practical next steps.

Speech Recognition in AI: how does free course learning support practice?

It links datasets and metrics with examples, so practice feels connected to the course instead of becoming random exercises without a clear learning goal. It also keeps the learning focused on practical next steps.

Which Speech Recognition in AI skills matter most after the basics?

Focus on model choices, analysis workflow, and explaining your steps. Those skills help you use the course in projects, interviews, assignments, or work situations. It also keeps the learning focused on practical next.

Where can Speech Recognition in AI be useful outside the lessons?

You can use it in data roles, small projects, workplace discussions, and study tasks where datasets or metrics affects the quality of your decision. It also keeps the learning focused on practical next steps.

Speech Recognition in AI: why start with a free course before advanced study?

A free course helps you confirm interest, learn the foundations, and see whether Speech Recognition in AI fits your career goal, project plan, or next paid program. It also keeps the learning focused on practical next.

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