Speech Recognition and Processing

Transform Voice into Text with Advanced Speech Recognition and Processing Techniques

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MODE
Online/ e-LMS

TYPE
Mentor Based

LEVEL
Moderate

DURATION
4 Weeks

About

This program covers key concepts in speech signal processing, Automatic Speech Recognition (ASR), and natural language understanding. Participants will explore deep learning models like RNNs and CNNs for speech recognition, voice command systems, and speech synthesis. Additionally, the course includes practical sessions on implementing ASR systems using Python-based libraries.

Aim

To provide an advanced understanding of speech recognition systems and signal processing techniques, enabling participants to develop AI-driven solutions for speech-to-text, voice commands, and natural language interfaces. This course focuses on modern algorithms, architectures, and real-world applications.

Program Objectives

Understand the principles of speech recognition and signal processing.
Learn how to build, train, and optimize speech-to-text models.
Explore speech synthesis and voice generation techniques.
Gain hands-on experience implementing speech recognition systems.
Understand the challenges and advancements in real-time speech processing.

Program Structure

Introduction to Speech Recognition and Processing
- Overview of Speech Recognition and Its Applications
- History and Evolution of Speech Technology
- Challenges in Speech Recognition (Accents, Noise, etc.)
Fundamentals of Speech Signals
- Speech Signal Characteristics
- Time-Domain and Frequency-Domain Representations
- Spectrograms and Waveforms
Signal Preprocessing Techniques
- Digital Signal Processing (DSP) Basics
- Feature Extraction: MFCCs (Mel-Frequency Cepstral Coefficients)
- Spectral Features and Filter Banks
Hidden Markov Models (HMMs) for Speech Recognition
- Introduction to HMMs
- Acoustic Models and Phoneme Recognition
- Decoding with HMMs for Speech Recognition Systems
Deep Learning for Speech Recognition
- Introduction to End-to-End Speech Recognition
- Convolutional Neural Networks (CNNs) in Speech
- Recurrent Neural Networks (RNNs), LSTMs, and GRUs for Sequential Speech Data
Automatic Speech Recognition (ASR) Systems
- ASR Architecture (Acoustic Model, Language Model)
- Speech-to-Text Pipeline (Data Flow from Speech to Recognized Text)
- Popular ASR Systems (e.g., Google ASR, DeepSpeech)
Language Models for Speech Recognition
- Statistical Language Models (n-grams)
- Neural Language Models (Transformers for Speech)
- Integration of Language Models with ASR Systems
Speaker Recognition and Identification
- Voice Biometrics: Speaker Identification and Verification
- Speaker Embeddings (e.g., i-Vectors, x-Vectors)
- Applications in Security and Personalization
Speech Synthesis and Text-to-Speech (TTS)
- Overview of Speech Synthesis
- WaveNet and Tacotron Architectures
- Real-World Applications of TTS (e.g., Voice Assistants)
Speech Enhancement and Noise Reduction
- Techniques for Speech Denoising
- Speech Enhancement with Deep Learning Models
- Real-Time Applications in Call Centers and Assistive Technologies
Ethics and Bias in Speech Technology
- Bias in ASR Systems (Gender, Accent, Dialect Biases)
- Ethical Considerations in Voice Data Collection
- Privacy Issues in Speech-Enabled Systems

Intended For

AI researchers, data scientists, machine learning engineers, and academicians working on natural language interfaces or voice-enabled AI systems.

Program Outcomes

Proficiency in building and optimizing speech recognition systems.
Understanding of advanced speech signal processing and deep learning techniques.
Hands-on experience with Python-based ASR systems and TTS models.
Ability to integrate speech recognition with natural language understanding systems.

Mentors

AI Mentor

Rajnish Tandon

Bodhi Nexus (Founder)

Biography

AI Mentor

Pratish Jain

Rajiv Gandhi Proudyogiki Vishwavidyalaya

Biography

AI Mentor

J. T. Sibychen

Cyber and Cloud Security Trainer

NIIT Foundation

Biography

More Mentors

Fee Structure

Fee: INR 10,999 USD 164

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