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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

    AI Applications: Computer Vision and Speech Recognition

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

    Intermediate · Course · 1 - 4 Weeks

  • Status: Free Trial
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    Edureka

    Mastering AI: Neural Nets, Vision System, Speech Recognition

    Skills you'll gain: Supervised Learning, Computer Vision, Recurrent Neural Networks (RNNs), Machine Learning Methods, Convolutional Neural Networks, Matplotlib, Data Visualization, Probability & Statistics, Deep Learning, Classification Algorithms, Artificial Intelligence, Plotly, Statistical Analysis, Data Visualization Software, Statistical Hypothesis Testing, Machine Learning, Seaborn, Applied Machine Learning, Digital Signal Processing, Statistical Inference

    3.3
    Rating, 3.3 out of 5 stars
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    6 reviews

    Intermediate · Specialization · 3 - 6 Months

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    SkillsBooster Academy

    AI Sound Generation for Business Communication

    Skills you'll gain: Generative AI, AI Product Strategy, Brand Strategy, Content Creation, Business Communication, Storytelling, Media Production, Brand Awareness, Social Media Content, Digital Design, Business Ethics, Marketing Communications, Creative Thinking, Influencing, Business Transformation, Entrepreneurship, Creative Problem-Solving, Media and Communications, Customer Engagement, Digital Marketing

    Beginner · Course · 1 - 3 Months

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    EDUCBA

    Master AWS Rekognition: Analyze, Detect, and Automate

    Skills you'll gain: Serverless Computing, Computer Vision, Image Analysis, Amazon Web Services, AI Enablement, Amazon S3, AWS Identity and Access Management (IAM), Text Mining, Data Processing, Unstructured Data

    Intermediate · Course · 1 - 4 Weeks

  • Status: Free Trial
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    DeepLearning.AI

    Deep Learning

    Skills you'll gain: Convolutional Neural Networks, Recurrent Neural Networks (RNNs), Computer Vision, Transfer Learning, Deep Learning, Image Analysis, Hugging Face, Natural Language Processing, Artificial Neural Networks, Tensorflow, Embeddings, Supervised Learning, Keras (Neural Network Library), Applied Machine Learning, Machine Learning, MLOps (Machine Learning Operations), Debugging, Performance Tuning, PyTorch (Machine Learning Library), Data Preprocessing

    Build toward a degree

    4.8
    Rating, 4.8 out of 5 stars
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    147K reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: Free Trial
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    IBM

    Gen AI Foundational Models for NLP & Language Understanding

    Skills you'll gain: PyTorch (Machine Learning Library), Large Language Modeling, Embeddings, Generative AI, Natural Language Processing, Transfer Learning, Recurrent Neural Networks (RNNs), Data Ethics, Artificial Neural Networks, Classification Algorithms, Model Evaluation, Data Preprocessing, Feature Engineering

    4.5
    Rating, 4.5 out of 5 stars
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    175 reviews

    Intermediate · Course · 1 - 4 Weeks

What brings you to Coursera today?

  • Status: New
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    Edureka

    Mastering NLP: Tokenization, Sentiment Analysis & Neural MT

    Skills you'll gain: Natural Language Processing, Large Language Modeling, Model Evaluation, Recurrent Neural Networks (RNNs), Classification Algorithms, Data Ethics, Responsible AI, Text Mining, Transfer Learning, Machine Learning Methods, PyTorch (Machine Learning Library), Artificial Neural Networks, Data Preprocessing, Artificial Intelligence and Machine Learning (AI/ML), Deep Learning, Data Processing, Embeddings, Machine Learning, Data Analysis, Data Cleansing

    Intermediate · Specialization · 3 - 6 Months

  • Next level skills. New Year savings.

    Save on Coursera Plus
  • Status: Free Trial
    Free Trial
    M

    Microsoft

    Natural Language Processing in Microsoft Azure

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

    4.7
    Rating, 4.7 out of 5 stars
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    121 reviews

    Beginner · Course · 1 - 4 Weeks

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    Coursera

    Voice AI: Introduction to Building Voice Applications

    Skills you'll gain: Case Studies, User Experience Design, Business Analysis, Natural Language Processing, Application Programming Interface (API), Application Deployment, Application Development, Artificial Intelligence, Systems Integration, Scalability

    Beginner · Course · 1 - 4 Weeks

  • Status: New
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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, Generative Adversarial Networks (GANs), Vision Transformer (ViT), Image Analysis, Embeddings, Autoencoders, Convolutional Neural Networks, Responsible AI, Computer Vision, Music, Recurrent Neural Networks (RNNs)

    Mixed · 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, Model Evaluation, Embeddings, Text Mining, Artificial Intelligence, Recurrent Neural Networks (RNNs), Artificial Neural Networks, Deep Learning, Machine Learning, Unsupervised Learning, Dimensionality Reduction

    Mixed · Course · 1 - 3 Months

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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, Machine Learning Software, Artificial Intelligence and Machine Learning (AI/ML), Deep Learning, Classification Algorithms, Logistic Regression, Unstructured Data, Model Evaluation, Dimensionality Reduction

    Intermediate · Course · 1 - 3 Months

What brings you to Coursera today?

Searches related to speech recognition

ai applications: computer vision and speech recognition
mastering ai: neural nets, vision system, speech recognition
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In summary, here are 10 of our most popular speech recognition courses

  • AI Applications: Computer Vision and Speech Recognition: Edureka
  • Mastering AI: Neural Nets, Vision System, Speech Recognition: Edureka
  • AI Sound Generation for Business Communication: SkillsBooster Academy
  • Master AWS Rekognition: Analyze, Detect, and Automate: EDUCBA
  • Deep Learning: DeepLearning.AI
  • Gen AI Foundational Models for NLP & Language Understanding: IBM
  • Mastering NLP: Tokenization, Sentiment Analysis & Neural MT: Edureka
  • Natural Language Processing in Microsoft Azure: Microsoft
  • Voice AI: Introduction to Building Voice Applications: Coursera
  • Generative AI for Audio and Images: Models and Applications: Alberta Machine Intelligence Institute

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Frequently Asked Questions about Speech Recognition

Speech recognition is a technology that enables machines to understand and process human speech. It converts spoken language into text, allowing for a range of applications from virtual assistants to transcription services. The importance of speech recognition lies in its ability to enhance accessibility, streamline communication, and improve user experience across various platforms. As more industries adopt this technology, understanding speech recognition becomes crucial for professionals looking to stay relevant in a rapidly evolving digital landscape.‎

Careers in speech recognition span various fields, including technology, healthcare, and education. Potential job roles include speech recognition engineer, data scientist specializing in natural language processing, and software developer for voice-activated applications. Additionally, positions in user experience design and accessibility consulting are also available, as companies seek to create more inclusive products. With the growing demand for voice technology, opportunities in this area are expanding.‎

To pursue a career in speech recognition, you should develop a blend of technical and analytical skills. Key areas include programming languages such as Python, understanding machine learning algorithms, and familiarity with natural language processing (NLP). Additionally, knowledge of audio signal processing and data analysis can be beneficial. Soft skills, such as problem-solving and effective communication, are also important as they enable collaboration in multidisciplinary teams.‎

Some of the best online courses for learning about speech recognition include the Mastering AI: Neural Nets, Vision System, Speech Recognition Specialization and the AI Applications: Computer Vision and Speech Recognition. These courses provide foundational knowledge and practical skills necessary for working with speech recognition technologies.‎

Yes. You can start learning speech recognition on Coursera for free in two ways:

  1. Preview the first module of many speech recognition courses at no cost. This includes video lessons, readings, graded assignments, and Coursera Coach (where available).
  2. Start a 7-day free trial for Specializations or Coursera Plus. This gives you full access to all course content across eligible programs within the timeframe of your trial.

If you want to keep learning, earn a certificate in speech recognition, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎

To learn speech recognition, start by exploring online courses that cover the fundamentals of the technology. Engage with interactive content, participate in discussions, and work on practical projects to reinforce your understanding. Additionally, consider joining online communities or forums where you can connect with others interested in speech recognition. This collaborative approach can enhance your learning experience and provide valuable insights.‎

Typical topics covered in speech recognition courses include the basics of audio processing, machine learning techniques, natural language processing, and the development of speech recognition systems. You may also explore advanced topics such as deep learning applications in speech recognition and the ethical considerations surrounding voice technology. These subjects provide a comprehensive understanding of how speech recognition works and its applications.‎

For training and upskilling employees in speech recognition, consider courses like the AI Workflow: Machine Learning, Visual Recognition and NLP. Such programs are designed to equip professionals with the necessary skills to implement speech recognition technologies effectively in their organizations. These courses can help enhance productivity and innovation within the workforce.‎

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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