Speech Recognition AI Training in Gurgaon

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Speech Recognition AI Course Overview

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Upcoming Speech Recognition AI Batches

21-09-2026
Mon-FriWeekdays Regular
08:00 AM & 10:00 AM Batches(Class 1Hr - 2Hrs) / Per Session
23-09-2026
Mon - FriWeekdays Regular
06:00 PM & 08:00 PM Batches(Class 1Hr - 2Hrs) / Per Session
25-09-2026
Sat-SunWeekend Batch
09:00 AM & 01:00 PM Batches(Class 2Hr - 4Hrs) / Per Session
Can't find a batch? Pick your own schedule

Speech Recognition AI Course Syllabus

Join our Data Modeling Training in Delhi! Our syllabus covers essential data modeling methodologies, tools, and advanced techniques. Our practical projects are led by industry experts, helping you to analyze data processes effectively in this growing tech hub.

  • Introduction to Speech Recognition and Automatic Speech Recognition (ASR)
  • Speech-to-Text (STT) systems
  • Speech production and perception
  • Components of an ASR system
  • AI and Machine Learning in speech technologies
  • Applications, challenges, and future of speech AI
  • Fundamentals of digital audio
  • Sampling and quantization
  • Speech waveforms and audio formats
  • Fourier Transform and Fast Fourier Transform (FFT)
  • Short-Time Fourier Transform (STFT)
  • Spectrograms and Mel Spectrograms
  • Mel scale and MFCC features
  • Audio preprocessing and normalization
  • Phonetics, phonemes, and pronunciation
  • Statistical speech recognition
  • Hidden Markov Models (HMM)
  • Gaussian Mixture Models (GMM)
  • Acoustic models
  • Language models
  • Pronunciation models
  • Decoding and search algorithms
  • Neural networks for speech processing
  • CNN-based speech models
  • RNN, LSTM, and GRU
  • Sequence-to-sequence learning
  • Connectionist Temporal Classification (CTC)
  • Attention mechanisms
  • Encoder-decoder architectures
  • End-to-end speech recognition
  • Transformer architecture
  • Self-attention and multi-head attention
  • Positional encoding
  • Transformer-based ASR
  • Conformer architecture
  • Encoder-only and encoder-decoder models
  • Beam search and decoding
  • Advantages of Transformer-based ASR
  • Self-supervised learning for speech
  • wav2vec 2.0
  • HuBERT
  • WavLM
  • XLS-R
  • Whisper
  • Transfer learning
  • Speech foundation models
  • Pre-training and fine-tuning concepts
  • Whisper architecture and workflow
  • Speech transcription using Whisper
  • Multilingual transcription
  • Speech translation
  • Timestamp generation
  • Prompting and decoding options
  • Batch transcription
  • Real-time transcription concepts
  • Practical Whisper applications
  • Speech dataset collection
  • Audio and transcript preparation
  • Data cleaning and validation
  • Audio-text alignment
  • Tokenization
  • Fine-tuning Whisper and Wav2Vec2
  • Data augmentation
  • SpecAugment
  • Handling noisy speech
  • Low-resource ASR
  • Multilingual ASR
  • Cross-lingual transfer learning
  • Code-switching and mixed-language speech
  • Accent and dialect handling
  • Low-resource language recognition
  • Indian-language speech datasets
  • Challenges in Indian-language ASR
  • Building multilingual speech applications
  • Word Error Rate (WER)
  • Character Error Rate (CER)
  • Accuracy and latency
  • Error analysis
  • Robustness testing
  • Noise and accent evaluation
  • Model optimization
  • Inference performance
  • Benchmarking ASR systems
  • Integration of ASR with Large Language Models (LLMs)
  • Speech-to-LLM pipelines
  • Speech-language models
  • Voice-based conversational AI
  • Speech-to-speech systems
  • Voice assistants
  • Retrieval-Augmented Generation (RAG) with voice
  • Multimodal speech AI
  • Streaming speech recognition
  • Real-time ASR architecture
  • Python-based ASR applications
  • ASR API integration
  • GPU and CPU inference
  • Model compression and optimization
  • Edge and on-device speech recognition
  • Cloud deployment
  • Privacy and responsible AI
  • Voice Activity Detection (VAD)
  • Speaker identification
  • Speaker diarization
  • Emotion recognition
  • Keyword spotting
  • Meeting transcription
  • Automatic subtitle generation
  • Voice search
  • Accessibility applications
  • Voice assistants and conversational systems
Need customized curriculum?
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Hands On Speech Recognition AI Projects

Enroll in our Speech Recognition AI Course, Build practical Speech Recognition AI solutions using modern ASR technologies such as Whisper, Wav2Vec2, Transformers, Python, and deep learning. Students will work with real-world speech data to develop, fine-tune, evaluate, and deploy intelligent speech-to-text applications.

Real-Time Speech-to-Text Transcription System

Develop a real-time speech recognition application that captures audio through a microphone and converts spoken language into text. The project uses modern ASR models such as Whisper and includes audio preprocessing, transcription, timestamp generation, noise handling, and real-time output. Students will learn how to build a complete speech-to-text pipeline and deploy it as a practical application.

Multilingual Meeting Transcription & Speaker Diarization

Build an AI-powered meeting transcription system that converts recorded conversations into searchable text while identifying different speakers. The system can process multilingual conversations, generate timestamps, separate speakers, and organize the final transcript. Students will explore speech recognition, speaker diarization, language detection, and post-processing to create an intelligent meeting documentation solution.

Custom Speech Recognition Model for Indian Languages

Develop a customized speech recognition system for an Indian language using a pre-trained model such as Wav2Vec2 or Whisper. Students will prepare a speech dataset, clean and preprocess audio, fine-tune the model, evaluate performance using WER/CER, and test the system on real-world speech with different accents and noise conditions.

For Corporates

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Our Instructor
Name
Mr. Kishore
Experience
7+ Years
Specialized in
Speech Recognition AI, Automatic Speech Recognition (ASR), Speech-to-Text Systems, Deep Learning, Transformer-Based Speech Models.
More Details
Mr. Kishore is a Speech Recognition AI professional with 7+ years of industry experience, specializing in ASR, Speech-to-Text, Deep Learning, Transformers, Whisper, Wav2Vec2, NLP, audio processing, and AI application development.

Speech Recognition AI Course Objectives

Our Speech Recognition AI Course provides practical knowledge of Speech Recognition AI, covering speech processing, ASR fundamentals, deep learning, Transformers, Whisper, Wav2Vec2, multilingual speech recognition, model fine-tuning, evaluation, and real-world AI application development.

Speech Recognition AI is a technology that uses artificial intelligence and machine learning to convert human speech or audio into written text.

Basic Python knowledge is recommended because practical exercises involve audio processing, machine learning models, ASR libraries, model training, and application development.

Whisper is a speech recognition model that can perform speech transcription and speech translation across multiple languages and is widely used for practical speech-to-text applications.

Wav2Vec2 is a self-supervised speech representation model that can be fine-tuned for tasks such as automatic speech recognition and speech classification.

Job Assistance Program

Our Job Assistance Programme offers you special guidance through the course curriculum and helps in your interview preparation.

Specialised Curriculum
Get on-field knowledge and skills from our expert instructors.
Assessment
Upgrade your on-field skills with our assessments and track your progress in real time.
Hands-on Project
Our hands-on project help you gain experience in real-time working.
Certification Guidance
A global certificate always helps you stand out from the crowd.
Portfolio Building
Experts guide you to maximise your profile with current industry trends that employers expect.
Placment Cell
We promote your abilities and showcase your portfolio to employers.

Speech Recognition AI Career Opportunity

Speech Recognition AI offers career opportunities as an ASR Engineer, Speech AI Engineer, Machine Learning Engineer, NLP Engineer, Voice AI Developer, and AI Researcher, with salaries typically ranging from ₹4–25+ LPA in India depending on skills, experience, and employer.

Annual Pay Scale
Placement Designation
Employers
Annual Salary
Hiring Companies

Placement Guidance & Interview Preparation

Infibee’s placement guidance navigates you to your desired role in top organisations, ensuring you stand out and excel in every opportunity.

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Speech Recognition AI Course FAQ's

Infibee Speech Recognition AI Course offers wide range of services that suits for both fresher and experienced persons via both offline and online at your suitable time slots.

You need not worry about having missed a class. Our dedicated course coordinator will help them with anything and everything related to administration. The coordinator will arrange a session for the student with trainers in place of the missed one.

Yes, of course. You can contact our team at Infibee Technologies, and we will schedule a free demo or a conference call with our mentor for you.

We provide classroom, online, and self-based study material and recorded sessions for students based on their individual preferences.

Yes, all our trainers are industry professionals with extensive experience in their respective domains. They bring hands-on practical and real-world knowledge to the training sessions.

Yes, participants typically receive access to course materials, including recorded sessions, assignments, and additional resources, even after the training concludes.

We provide placement assistance to students, including resume building, interview preparation, and job placement support for a wide range of software courses.

Yes, we offer customisation of the syllabus for both individual candidates and corporate also.

Yes, we offer corporate training solutions. Companies can contact us for customised programmes tailored to their team’s needs.

Participants need a stable internet connection and a device (computer, laptop, or tablet) with the necessary software installed. Detailed technical requirements are provided upon enrollment.

In most cases, such requests can be accommodated. Participants can reach out to our support team to discuss their preferences and explore available options.

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