Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Speech Recognition and Processing teaches how machines turn sound into meaning. You begin with the signal itself — sampling, spectrograms and features such as MFCCs — then learn how acoustic and language models combine to transcribe speech. The course moves to the modern approach: end-to-end neural recognisers and powerful pretrained models such as Wav2Vec and Whisper that you fine-tune for your own audio. Alongside recognition you cover related tasks like speaker identification and keyword spotting, finishing able to build and evaluate a working speech-to-text pipeline. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers speech processing and automatic speech recognition — audio features, acoustic and language modelling, and modern end-to-end and pretrained speech models.
📋 Course Objectives
1. Extract audio features such as spectrograms and MFCCs.
2. Explain acoustic and language modelling in ASR.
3. Build or fine-tune end-to-end speech-recognition models.
4. Apply pretrained models such as Wav2Vec and Whisper.
5. Evaluate transcription quality with word error rate.
👥 Who Should Enroll?
• Developers building voice interfaces
• Data scientists working with audio
• Researchers in speech and language technology
• Students specialising in audio AI
🚀 Key Learning Outcomes
• A working speech-to-text pipeline.
• The ability to fine-tune modern speech models.
• A voice-processing project for your portfolio.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Speech Recognition And Processing Foundations
Apply mathematical concepts such as linear algebra and calculus to analyze speech signals and develop foundational models • Design and implement basic speech recognition systems using machine learning libraries and frameworks • Evaluate the performance of simple speech recognition models using metrics such as accuracy and F1-score
Data Engineering, Preprocessing, and Feature Pipelines
Develop and deploy data pipelines to preprocess and feature-engineer large speech datasets using tools such as Apache Beam and Spark • Configure and optimize data storage solutions such as relational databases and NoSQL databases for efficient speech data management • Analyze and visualize speech data distributions and patterns using statistical and machine learning techniques
Model Architecture, Algorithm Design, and Speech Recognition And Processing Methods
Design and implement deep learning architectures such as convolutional neural networks and recurrent neural networks for speech recognition tasks • Develop and evaluate speech recognition algorithms using techniques such as hidden Markov models and dynamic time warping • Optimize model performance using hyperparameter tuning and regularization techniques such as dropout and early stopping
Training, Hyperparameter Optimization, and Evaluation
Train and evaluate speech recognition models using large datasets and distributed computing frameworks such as TensorFlow and PyTorch • Implement hyperparameter optimization techniques such as grid search and random search to improve model performance • Analyze and visualize model performance using metrics such as accuracy, precision, and recall
Deployment, MLOps, and Production Workflows
Deploy speech recognition models in production environments using containerization tools such as Docker and Kubernetes • Develop and implement MLOps pipelines to automate model training, deployment, and monitoring • Configure and optimize model serving infrastructure using tools such as TensorFlow Serving and AWS SageMaker
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze and mitigate bias in speech recognition models using techniques such as data augmentation and debiasing • Develop and implement responsible AI practices such as transparency, explainability, and fairness • Evaluate the ethical implications of speech recognition systems and develop strategies for addressing potential issues
Industry Integration, Business Applications, and Case Studies
Develop and deploy speech recognition systems for real-world applications such as virtual assistants and voice-controlled devices • Analyze and evaluate the business value of speech recognition systems using case studies and industry reports • Design and implement speech recognition solutions for specific industries such as healthcare and finance
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Docker |
| Covered Tool / Platform | Kubernetes |
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