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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
    Free Trial
    E

    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, Plot (Graphics), Matplotlib, Data Visualization, Probability & Statistics, Deep Learning, AI Personalization, Classification Algorithms, Artificial Intelligence, Plotly, Statistical Analysis, Statistical Methods, Machine Learning, Applied Machine Learning, Digital Signal Processing, Statistical Inference

    3.8
    Rating, 3.8 out of 5 stars
    ·
    8 reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    E

    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

  • G

    Google Cloud

    Transformer Models and BERT Model

    Skills you'll gain: Large Language Modeling, Natural Language Processing, Generative Model Architectures, Transfer Learning

    4.1
    Rating, 4.1 out of 5 stars
    ·
    125 reviews

    Advanced · Course · 1 - 4 Weeks

  • Status: New
    New
    Status: Free Trial
    Free Trial
    A

    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), AI powered creativity, Autoencoders, Convolutional Neural Networks, Model Training, Responsible AI, Computer Vision, Music, Digital Signal Processing

    Mixed · Course · 1 - 4 Weeks

  • Status: Preview
    Preview
    N

    Northeastern University

    Applied Natural Language Processing in Engineering Part 1

    Skills you'll gain: Natural Language Processing, Artificial Intelligence and Machine Learning (AI/ML), Model Evaluation, Model Optimization, Embeddings, Machine Learning Methods, Artificial Neural Networks, Deep Learning, Machine Learning, Dependency Analysis, Supervised Learning

    Mixed · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    M

    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), AI literacy, Unstructured Data, Automation, Application Development

    4.7
    Rating, 4.7 out of 5 stars
    ·
    125 reviews

    Beginner · Course · 1 - 4 Weeks

What brings you to Coursera today?

  • Status: Free Trial
    Free Trial
    I

    IBM

    IBM AI Engineering

    Skills you'll gain: Prompt Engineering, Apache Spark, Large Language Modeling, Retrieval-Augmented Generation, Transfer Learning, Model Evaluation, Computer Vision, PyTorch (Machine Learning Library), Unsupervised Learning, Generative Model Architectures, Generative AI, PySpark, Prompt Engineering Tools, Vision Transformer (ViT), Keras (Neural Network Library), Vector Databases, Fine-tuning, Machine Learning, Python Programming, Data Science

    Build toward a degree

    4.6
    Rating, 4.6 out of 5 stars
    ·
    22K reviews

    Intermediate · Professional Certificate · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    D

    DeepLearning.AI

    Deep Learning

    Skills you'll gain: Convolutional Neural Networks, Recurrent Neural Networks (RNNs), Computer Vision, Transfer Learning, Deep Learning, Image Analysis, Model Optimization, Hugging Face, Natural Language Processing, Artificial Neural Networks, Artificial Intelligence and Machine Learning (AI/ML), Tensorflow, Applied Machine Learning, Model Training, Fine-tuning, Generative AI, Embeddings, Supervised Learning, Large Language Modeling, Artificial Intelligence

    Build toward a degree

    4.8
    Rating, 4.8 out of 5 stars
    ·
    147K reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: New
    New
    Status: Free Trial
    Free Trial
    C

    Coursera

    Pixels, Waveforms & Words: Engineering Multimodal AI Systems

    Skills you'll gain: Model Optimization, Image Analysis, Transfer Learning, Fine-tuning, Multimodal Prompts, Model Evaluation, Technical Documentation, Data Preprocessing, Debugging, Computer Vision, PyTorch (Machine Learning Library), Feature Engineering, Tensorflow, Model Training, Embeddings, Systems Design, Ethical Standards And Conduct, Large Language Modeling, Root Cause Analysis, Deep Learning

    Intermediate · Specialization · 3 - 6 Months

  • Status: New
    New
    Status: Free Trial
    Free Trial
    C

    Coursera

    Tokens to Deployment: NLP, Language Models, & Production API

    Skills you'll gain: OAuth, Unit Testing, Apache Airflow, Fine-tuning, Model Evaluation, Data Validation, API Design, Test Driven Development (TDD), Data Integrity, Extract, Transform, Load, Multimodal Prompts, Natural Language Processing, MLOps (Machine Learning Operations), OpenAI API, Data Presentation, Hugging Face, Token Optimization, LLM Application, CI/CD, Tensorflow

    Intermediate · Specialization · 3 - 6 Months

  • Status: New
    New
    Status: Free Trial
    Free Trial
    C

    Coursera

    Vision & Audio AI Systems

    Skills you'll gain: Apache Airflow, Model Optimization, Data Validation, Image Analysis, Transfer Learning, Data Preprocessing, Data Integrity, Model Evaluation, Debugging, Computer Vision, PyTorch (Machine Learning Library), Data Pipelines, Feature Engineering, MLOps (Machine Learning Operations), Tensorflow, Model Training, Embeddings, Performance Tuning, Deep Learning, Digital Signal Processing

    Advanced · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    D

    DeepLearning.AI

    Natural Language Processing

    Skills you'll gain: Natural Language Processing, Supervised Learning, Transfer Learning, Recurrent Neural Networks (RNNs), Markov Model, Embeddings, Applied Machine Learning, Dimensionality Reduction, Large Language Modeling, Text Mining, Statistical Machine Learning, Fine-tuning, Artificial Neural Networks, Classification Algorithms, Data Preprocessing, Deep Learning, Tensorflow, Machine Learning Methods, Logistic Regression, Feature Engineering

    4.6
    Rating, 4.6 out of 5 stars
    ·
    6.2K reviews

    Intermediate · Specialization · 3 - 6 Months

1234…101

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
  • Transformer Models and BERT Model: Google Cloud
  • Generative AI for Audio and Images: Models and Applications: Alberta Machine Intelligence Institute
  • Applied Natural Language Processing in Engineering Part 1: Northeastern University
  • Natural Language Processing in Microsoft Azure: Microsoft
  • IBM AI Engineering: IBM
  • Deep Learning: DeepLearning.AI
  • Pixels, Waveforms & Words: Engineering Multimodal AI Systems: Coursera
  • Tokens to Deployment: NLP, Language Models, & Production API: Coursera

Skills you can learn in Design And Product

User Interface (18)
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Agile Software Development (10)
Graphics (10)
Virtual Reality (9)
Design Thinking (8)
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Video Game Development (7)
Web Design (7)
Adobe Photoshop (6)

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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