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Speech Recognition Courses

Speech recognition courses can help you learn the fundamentals of audio processing, language modeling, and transcription techniques. You can build skills in feature extraction, acoustic modeling, and implementing neural networks for voice recognition. Many courses introduce tools like Python libraries, TensorFlow, and Kaldi, that support developing speech recognition systems and applying AI to enhance user interactions in applications such as virtual assistants and automated transcription services.


Popular Speech Recognition Courses and Certifications


  • Status: Free Trial
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    Edureka

    Mastering AI: Neural Nets, Vision System, Speech Recognition

    Skills you'll gain: Supervised Learning, Computer Vision, Machine Learning Methods, Matplotlib, Data Visualization, Probability & Statistics, Deep Learning, Statistical Machine Learning, Artificial Intelligence, Plotly, Statistical Analysis, Data Visualization Software, Statistical Hypothesis Testing, Machine Learning, Seaborn, Data Science, Programming Principles, Python Programming, Applied Machine Learning, Statistical Inference

    Intermediate · Specialization · 3 - 6 Months

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    Edureka

    AI Applications: Computer Vision and Speech Recognition

    Skills you'll gain: Computer Vision, Applied Machine Learning

    Intermediate · Course · 1 - 4 Weeks

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    DeepLearning.AI

    Deep Learning

    Skills you'll gain: Computer Vision, Deep Learning, Image Analysis, Natural Language Processing, Artificial Neural Networks, Tensorflow, Generative AI, Supervised Learning, Large Language Modeling, Artificial Intelligence and Machine Learning (AI/ML), Artificial Intelligence, Applied Machine Learning, PyTorch (Machine Learning Library), Machine Learning, MLOps (Machine Learning Operations), Debugging, Performance Tuning, Python Programming, Data-Driven Decision-Making, Machine Learning Algorithms

    Build toward a degree

    4.8
    Rating, 4.8 out of 5 stars
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    147K reviews

    Intermediate · Specialization · 3 - 6 Months

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    Edureka

    Mastering NLP: Tokenization, Sentiment Analysis & Neural MT

    Skills you'll gain: Natural Language Processing, Large Language Modeling, Data Ethics, Responsible AI, Text Mining, PyTorch (Machine Learning Library), Artificial Neural Networks, Tensorflow, Artificial Intelligence and Machine Learning (AI/ML), Deep Learning, Data Processing, Artificial Intelligence, Scikit Learn (Machine Learning Library), Time Series Analysis and Forecasting, Machine Learning, Data Analysis, Unstructured Data, Data Cleansing, Applied Machine Learning, Unified Modeling Language

    Intermediate · Specialization · 3 - 6 Months

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    Alberta Machine Intelligence Institute

    Generative AI for Audio and Images: Models and Applications

    Skills you'll gain: Generative AI, Generative Model Architectures, Image Analysis, Artificial Intelligence and Machine Learning (AI/ML), Responsible AI, Computer Vision, Creativity, Deep Learning, Music, Data Ethics

    Mixed · Course · 1 - 4 Weeks

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    IBM

    Gen AI Foundational Models for NLP & Language Understanding

    Skills you'll gain: PyTorch (Machine Learning Library), Large Language Modeling, Generative AI, Natural Language Processing, Data Ethics, Text Mining, Artificial Neural Networks, Deep Learning, Feature Engineering, Statistical Methods

    4.5
    Rating, 4.5 out of 5 stars
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    168 reviews

    Intermediate · Course · 1 - 4 Weeks

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    Northeastern University

    Applied Natural Language Processing in Engineering Part 1

    Skills you'll gain: Natural Language Processing, Artificial Intelligence, Artificial Neural Networks, Deep Learning, Machine Learning, Unstructured Data, Supervised Learning, Linear Algebra, Probability Distribution

    Mixed · Course · 1 - 3 Months

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    Microsoft

    Natural Language Processing in Microsoft Azure

    Skills you'll gain: Text Mining, Natural Language Processing, Microsoft Azure, Artificial Intelligence, Artificial Intelligence and Machine Learning (AI/ML), Application Development

    4.7
    Rating, 4.7 out of 5 stars
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    120 reviews

    Beginner · Course · 1 - 4 Weeks

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    DeepLearning.AI

    Generative AI with Large Language Models

    Skills you'll gain: Generative AI, Large Language Modeling, Prompt Engineering, PyTorch (Machine Learning Library), Python Programming, Applied Machine Learning, Scalability, Natural Language Processing, Responsible AI, Machine Learning, Reinforcement Learning, Performance Tuning

    4.8
    Rating, 4.8 out of 5 stars
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    3.5K reviews

    Intermediate · Course · 1 - 4 Weeks

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    Google Cloud

    Create Image Captioning Models

    Skills you'll gain: Generative AI, Image Analysis, Generative Model Architectures, Deep Learning, Computer Vision

    4.7
    Rating, 4.7 out of 5 stars
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    32 reviews

    Advanced · Course · 1 - 4 Weeks

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    Northeastern University

    Statistical Learning for Engineering Part 1

    Skills you'll gain: Unsupervised Learning, Supervised Learning, Regression Analysis, Applied Machine Learning, Statistical Modeling, Machine Learning Algorithms, PyTorch (Machine Learning Library), Statistical Methods, Statistical Machine Learning, Machine Learning, Predictive Analytics, Predictive Modeling, Statistical Programming, Artificial Intelligence and Machine Learning (AI/ML), Deep Learning, Probability & Statistics, Dimensionality Reduction

    Intermediate · Course · 1 - 3 Months

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    Packt

    Conversation Design for Chatbots & Voice Assistants

    Skills you'll gain: Persona (User Experience), User Interface (UI), Prompt Engineering, Interaction Design, Usability, User Experience Design, Human Computer Interaction, Usability Testing, Web Content Accessibility Guidelines, AI Personalization, User Research, Prototyping, Natural Language Processing

    Intermediate · Course · 1 - 3 Months

Searches related to speech recognition

ai applications: computer vision and speech recognition
mastering ai: neural nets, vision system, speech recognition
1234…99

In summary, here are 10 of our most popular speech recognition courses

  • Mastering AI: Neural Nets, Vision System, Speech Recognition: Edureka
  • AI Applications: Computer Vision and Speech Recognition: Edureka
  • Deep Learning: DeepLearning.AI
  • Mastering NLP: Tokenization, Sentiment Analysis & Neural MT: Edureka
  • Generative AI for Audio and Images: Models and Applications: Alberta Machine Intelligence Institute
  • Gen AI Foundational Models for NLP & Language Understanding: IBM
  • Applied Natural Language Processing in Engineering Part 1: Northeastern University
  • Natural Language Processing in Microsoft Azure: Microsoft
  • Generative AI with Large Language Models: DeepLearning.AI
  • Create Image Captioning Models: Google Cloud

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Adobe Photoshop (6)

Frequently Asked Questions about Speech Recognition

Speech recognition refers to the process by which computer software translates human speech to a written, machine-readable format. This capability is used increasingly widely, in applications ranging from simple dictation and question-answering programs to tools for real-time foreign language translation and full-featured chatbots. Advanced speech recognition capabilities will be an important part of the future of computer software and mobile apps, allowing users to interact with software in an intuitive and hands-free way.

While the history of speech recognition dates back to the 1960s, progress in this field has accelerated greatly in the past decade with the advent of machine learning and deep learning. The use of these algorithmic approaches have opened the door for robust natural language processing (NLP), which goes beyond the simple conversion of spoken words into text and allows programs to understand the meaning of those words - and respond appropriately, if desired. NLP will be at the forefront of artificial intelligence (AI) applications, playing a key role in the functionality of helpful digital assistants and virtual agents as well as advanced robotics.‎

Speech recognition and natural language processing (NLP) programming skills are in high demand from a growing range of companies seeking to tap into this technology to create new products and services. Typically using Python programming and TensorFlow, machine learning and deep learning engineers with expertise in this field build models that analyze speech and language, discover contextual patterns, and produce insights and situationally appropriate responses. According to Glassdoor, NLP engineers earned an average annual salary of $114,121 as of November 2020.‎

Yes! Coursera has a variety of opportunities to learn about topics in machine learning and deep learning, including courses specifically on speech recognition and natural language processing (NLP). You can learn from top-ranked institutions in the field, like Stanford University and deeplearning.ai, leading companies like Google Cloud, or even by completing step-by-step tutorials alongside experienced instructors as part of Coursera’s Guided Projects. No matter how you choose to learn, you’ll be able to view course materials and complete assignments on a flexible schedule, which allows you to fit this valuable education in speech recognition into your existing studies, work, or family life.‎

The skills and experience you might need to already have before starting to learn speech recognition are right in front of you, in the form of your mobile device. You may already be using speech to text your friends and family. This is one of the clearest examples of speech recognition, and voice-to-text is an experience you are likely familiar with. You may also want to learn about voice-activated assistants, like Alexa, Siri, and Google Assistant. These three offer the latest technologies that may help you learn speech recognition.‎

The kind of people that are best suited for roles in speech recognition are those technology professionals who understand how software drives speech recognition. Software creators write programs to process human speech into written words on a screen. Speech recognition works as it focuses on translating speech from a verbal format to a text-based format. These people who are best suited for roles in speech recognition likely are also well versed in or want to learn about programming languages and artificial intelligence.‎

How do I know if learning speech recognition is right for me? You may know if learning speech recognition is right for you if you have a deep passion to work on the cutting edge of technology. Speech recognition technology has fast changed the way modern society communicates, and there is likely a great deal more learning that will come in this area. You may also want to use your software skills to write code to train computers in similar ways to how we train our own brains, using fundamental processes, innovative thinking, and data research. If you’re interested in learning speech recognition as a career, you might know that this is a field that is right for you.‎

Some topics that are related to speech recognition may include natural language processing (NLP), deep learning neural networks, machine learning, and artificial intelligence. You may also want to dive into each of the main speech recognition technologies offered by the major tech companies like Amazon, Google, Apple, Facebook, and others.‎

Online Speech Recognition courses offer a convenient and flexible way to enhance your knowledge or learn new Speech Recognition skills. Choose from a wide range of Speech Recognition courses offered by top universities and industry leaders tailored to various skill levels.‎

When looking to enhance your workforce's skills in Speech Recognition, it's crucial to select a course that aligns with their current abilities and learning objectives. Our Skills Dashboard is an invaluable tool for identifying skill gaps and choosing the most appropriate course for effective upskilling. For a comprehensive understanding of how our courses can benefit your employees, explore the enterprise solutions we offer. Discover more about our tailored programs at Coursera for Business here.‎

This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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