Introduction to Speech Recognition Technology
The Introduction to Speech Recognition Technology course provides comprehensive training in applying deep learning algorithms to speech data processing. Built around TensorFlow as the primary programming framework, the curriculum covers the complete journey from foundational neural network concepts through to advanced speech recognition applications. Learners develop practical skills in TensorFlow programming, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and NLP models including BERT, gaining the technical depth needed to build and deploy speech-based AI systems. The programme also addresses applied topics such as speech sentiment analysis, audio classification, and conversational agent development using DialogFlow. Language modelling and NLP fundamentals are woven throughout, ensuring learners understand both the theoretical underpinnings and practical implementation of modern speech recognition systems. Ideal for learners with an interest in AI, machine learning, and voice technology, this course prepares participants for roles in voice recognition system development, speech-enabled application engineering, and AI research.
₹ 15,590
Overview
The Introduction to Speech Recognition Technology course offered by TimesPro is a technically rigorous programme that equips learners with the deep learning foundations and applied skills required to build speech recognition systems. TensorFlow is the backbone of the technical curriculum, with extensive coverage spanning installation, core concepts, graph execution, Tensorboard, and the transition to TensorFlow 2.0 — ensuring learners are proficient with both the framework's evolution and its modern implementation. From this foundation, the programme builds through neural network theory, convolutional neural networks, and ultimately into the domain of speech recognition itself, covering the full stack from signal processing concepts through to deployed conversational agents. Applied capabilities covered include speech sentiment analysis using RNNs, audio classification using CNNs, chatbot development with DialogFlow, and language modelling with advanced NLP architectures including BERT. This breadth makes the programme relevant not only for speech recognition specialists but for any AI or ML practitioner looking to develop applied deep learning expertise. Delivered in a self-paced format through the TimesPro platform, the course is accessible to learners with a foundation in Python and machine learning who are ready to specialise in one of the most dynamic areas of applied AI.
Why Technology & Analytics?
Speech recognition is among the fastest-growing application domains in artificial intelligence, powering voice assistants, transcription services, conversational agents, accessibility tools, and a growing range of enterprise applications. Professionals who can design, implement, and optimise speech recognition systems are in strong demand. This programme addresses that need directly by combining a thorough TensorFlow grounding — one of the most widely used deep learning frameworks in industry — with specialised speech and NLP content. Unlike broader deep learning courses, this programme maintains a clear orientation toward speech and language throughout, ensuring that TensorFlow skills are developed in direct service of speech recognition and NLP goals. The inclusion of DialogFlow for conversational agent development and BERT for language modelling keeps the curriculum aligned with the tools and architectures actually used in production speech systems today. For learners who already have basic ML knowledge and want to specialise in voice and speech AI, this course provides a focused, practical pathway into one of the most impactful branches of the field.
What does this course have to offer?
Key Highlights
Comprehensive TensorFlow programming coverage from fundamentals to TensorFlow 2.0 Neural network foundations including CNN and RNN architectures Speech recognition and audio classification using deep learning Speech sentiment analysis with RNNs Conversational agent development with DialogFlow NLP and language modelling including BERT Self-paced format with certification on completion
Who is this programme for?
Aspiring AI and ML engineers interested in speech and voice technology Data scientists looking to specialise in NLP and speech recognition Software developers building voice-enabled or speech-based applications Learners with Python and ML basics seeking to develop deep learning specialisation Professionals targeting roles in voice recognition systems or conversational AI
Minimum Eligibility
Basic understanding of Python programming Familiarity with machine learning or deep learning fundamentals is beneficial Interest in AI, speech processing, or voice technology No prior TensorFlow or speech recognition experience required
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Admission Overview
Admission to the Introduction to Speech Recognition Technology course is open enrolment with no formal entry tests or interview process. Learners enrol through the TimesPro platform, complete payment, and receive immediate access to all course content. A basic understanding of Python and familiarity with machine learning fundamentals is recommended to engage effectively with the deep learning curriculum. The programme is structured across four modules — Programming with TF, Foundations of NN, Fundamentals of CNN, and Speech Recognition — progressing logically from TensorFlow programming basics through neural network theory and CNN architecture into applied speech recognition. Each module builds on the previous, ensuring a coherent and cumulative learning experience. Embedded quizzes and assignments reinforce understanding at each stage, and a TimesPro certificate is awarded upon successful completion of all programme requirements.
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Curriculum
The curriculum of the Introduction to Speech Recognition Technology course is organised across four modules that build progressively from TensorFlow programming fundamentals to advanced speech recognition applications. The first module establishes deep proficiency in TensorFlow, covering the framework's evolution, core programming constructs, graph execution, Tensorboard, TensorFlow 2.0 features, and practical matrix and equation-solving exercises. The second module develops foundational neural network knowledge, providing the theoretical and applied grounding needed for the more specialised content that follows. The third module addresses convolutional neural networks in depth, covering CNN architecture and its application to different problem types. The fourth and final module brings these capabilities to bear on speech recognition, covering speech processing, audio classification, sentiment analysis, DialogFlow conversational agents, language modelling, and NLP with BERT.
There are 4 semesters in this course
The first module, Programming with TF, is the most extensive in the programme and provides comprehensive coverage of TensorFlow as a deep learning framework. It begins with installation and an overview of TensorFlow's evolution, then progresses through core concepts including variables, constants, placeholders, and the graph execution model. Learners write practical programs including a Hello World implementation and matrix multiplication exercises, work with Tensorboard for visualisation, and transition into TensorFlow 2.0 — covering distributed execution, updated variable and constant handling, optimisers, and the upgrade toolchain. A dedicated quiz reinforces mastery of TensorFlow programming concepts. The second module, Foundations of NN, provides the theoretical and practical grounding in neural networks that underpins all subsequent deep learning content. It covers network architecture, forward and backward propagation, activation functions, and training methodologies. The third module, Fundamentals of CNN, introduces convolutional neural network architecture and its application across image and signal processing domains, establishing the CNN skills central to speech and audio classification tasks. The fourth module, Speech Recognition, is the programme's applied capstone, bringing together all prior learning to address real speech recognition challenges. It covers speech processing pipelines, audio classification using CNNs, speech sentiment analysis using RNNs, building conversational agents with DialogFlow, language model fundamentals, and NLP with BERT for advanced natural language understanding in speech contexts.
Programming with TF
Foundations of NN
Fundamentals of CNN
Speech Recognition
Programme Length
Self-paced; complete at your own schedule with no fixed deadlines
Whom you will learn from?
Learn from top industry experts who bring real-world experience and deep knowledge to every lesson. The instructors are dedicated to help you achieve your goals with practical insights and hands-on guidance.
Tuition Fee
The Introduction to Speech Recognition Technology course is priced at Rs. 15,590, payable as a one-time fee at the time of enrolment. Payment is processed through TimesPro's official and secure payment channels only. Learners are advised not to make payments through any channel outside the official platform. For payment queries, contact TimesPro.
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About the University
TimesPro, established in 2013, is a Higher EdTech platform by The Times Group focused on professional education in India. The institution offers diverse learning programs across early career courses, executive education, and enterprise solutions. Their programs span banking, finance, technology, analytics, and marketing sectors through partnerships with premier institutions like IIMs, IITs, and global universities
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TimesPro provides extensive career support through dedicated placement assistance and industry connections. The institution focuses on developing industry-ready professionals through practical training, skill development, and career guidance. Their placement cell offers comprehensive support including interview preparation, profile enhancement, and direct connections with leading employers. The career services team works closely with over 3,500 corporate partners to ensure strong placement outcomes and career growth opportunities for students. Students receive personalized mentoring, soft skills training, and industry-specific preparation to enhance their employability.
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