Mastering Speech Language Models : From Asr To Emotion Ai

Master cutting-edge SpeechLMs and build next-generation voice AI applications with end-to-end speech capabilities

What you'll learn

Develop end-to-end speech language models using Python and Transformer architectures.

Master audio feature extraction and tokenization for speech recognition and synthesis.

Build AI for emotion recognition and personalized speech with real-world applications.

Evaluate SpeechLMs with metrics like WER and explore ethical AI design practices.

Requirements

No prior speech AI experience required – beginner-friendly with hands-on guidance!

A computer with Python 3.7+, TensorFlow/PyTorch, and audio libraries (e.g., Librosa).

Basic Python programming (familiarity with loops, functions, and libraries like NumPy).

Description

Transform your understanding of voice AI with this comprehensive course on Speech Language Models (SLMs) - the revolutionary technology that's replacing traditional speech processing pipelines with powerful end-to-end solutions.What You'll Master:Speech Language Models represent the next frontier in AI, moving beyond the limitations of traditional ASR→LLM→TTS pipelines. This course takes you from fundamental concepts to advanced applications, covering everything from speech tokenization and transformer architectures to emotion AI and real-time voice interactions.Why This Course Matters:Traditional speech processing suffers from information loss, high latency, and error accumulation across multiple stages. SLMs solve these problems by processing speech directly, capturing not just words but emotions, speaker identity, and paralinguistic cues that make human communication rich and nuanced.What Makes This Course Unique:Hands-on Learning: Work with state-of-the-art models like YourTTS, Whisper, and HuBERTComplete Pipeline Coverage: From raw audio to deployed applicationsReal-world Applications: Build ASR systems, voice cloning, emotion recognition, and interactive voice agentsLatest Research: Covers cutting-edge developments in the rapidly evolving SLM fieldPractical Implementation: Learn training methodologies, evaluation metrics, and deployment strategiesKey Technologies You'll Work With:Speech tokenizers (EnCodec, HuBERT, Wav2Vec 2.0)Transformer architectures adapted for speechVocoder technologies (Hi-Fi GAN)Multi-modal training approachesParameter-efficient fine-tuning (LoRA)Perfect For:AI/ML engineers wanting to specialize in speech technologyStudents or Career ChangersResearchers exploring next-generation voice AIDevelopers building voice-first applicationsAnyone curious about how modern voice assistants really workCourse Outcome:By completion, you'll have the skills to design, train, and deploy Speech Language Models for diverse applications - from basic speech recognition to sophisticated emotion-aware voice agents. You'll understand both the theoretical foundations and practical implementation details needed to contribute to this exciting field.Join the voice AI revolution and master the technology that's reshaping human-computer interaction!

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Module 1: Introduction to Speech Language Processing and the Emergence of Speech

Lecture 2 Introduction to Module 1 -Intro to Speech LP and the Emergence of SpeechLM Model

Lecture 3 1.1 Overview of Traditional Speech Processing - Part 1

Lecture 4 1.1 Overview of Traditional Speech Processing - Part 2

Lecture 5 How to download Anaconda and create environment

Lecture 6 1.1 Coding Eg & Ex. Discussion - Building a Speech-Enabled Conversational Agent

Lecture 7 1.2 Limitations of the Traditional Pipeline - Part 1

Lecture 8 1.2 Limitations of the Traditional Pipeline - Part 2

Lecture 9 1.2 Coding Example Discussion - Speech Pipeline with Simulated Limitations

Lecture 10 1.3 Introduction to Speech Language Models (SpeechLMs) - Part 1

Lecture 11 1.3 Introduction to Speech Language Models (SpeechLMs) - Part 2

Lecture 12 Coding Eg & Ex Disc. 1.3- Audio Tokenization and Reconstruction + Multi-Bandwidt

Lecture 13 1.4 - Advantages of Speech Language Models (SpeechLMs) - Part 1

Lecture 14 1.4 - Advantages of Speech Language Models (SpeechLMs) - Part 2

Lecture 15 Coding Eg & Ex 1.4 - Speech & Emotion Recognition with SpeechLM - wav2vec2

Lecture 16 1.5 Contrast of SpeechLM with Text-based Language Models (TextLMs) - Part 1

Lecture 17 1.5 Contrast of SpeechLM with Text-based Language Models (TextLMs) - Part 2

Lecture 18 Coding Example Discussion 1.5 - TextLM vs. SpeechLM Modality Comparison

Lecture 19 1.6 Applications of Speech Language Models (SpeechLMs) - Part 1

Lecture 20 1.6 Applications of Speech Language Models (SpeechLMs) - Part 2

Lecture 21 Coding Example Discussion 1.6 - Emotion-Aware Speech Assistant

Section 3: Module 2: Fundamentals of Speech and Language for SpeechLMs

Lecture 22 Intro to Module 2 - Fundamentals of Speech and Language for SpeechLMs

Lecture 23 2.1 Basics of Speech Acoustics - Part 1

Lecture 24 2.1 Basics of Speech Acoustics - Part 2

Lecture 25 Code Eg & Ex 2.1 - Speech Analysis & Transcription + Speech Feature Extraction

Lecture 26 2.2 The Source-Filter Model of Speech Production - Part 1

Lecture 27 2.2 The Source-Filter Model of Speech Production - Part 2

Lecture 28 2.3 Phonetics and Phonology in Speech - Part 1

Lecture 29 2.3 Phonetics and Phonology in Speech - Part 2

Lecture 30 Code Eg Discussion - 2.3 Phonetic Recognition and Analysis System

Lecture 31 2.4 Audio Feature Extraction - Part 1

Lecture 32 2.4 Audio Feature Extraction - Part 2

Lecture 33 Coding Eg Discussion 2.4 - Noise Robustness in Speech Feature Analysis

Lecture 34 2.5 Cross-Modal Representations for Speech Language Models - Part 1

Lecture 35 2.5 Cross-Modal Representations for Speech Language Models - Part 2

Lecture 36 Code Eg & Ex 2.5 - Cross-Modal Alignment Visualization & Analysis Framework

Section 4: Module 3: Architectures and Key Components of SpeechLMs

Lecture 37 Introduction to Module 3 - Architectures and Key Components of SpeechLMs

Lecture 38 3.1 General Architecture of a SpeechLM - Part 1

Lecture 39 3.1 General Architecture of a SpeechLM - Part 2

Lecture 40 Code Eg & Ex 3.1 - Simplified SpeechLM Pipeline Simulation + w/ Bigram Language

Lecture 41 3.2 Speech Tokenizers - Part 1

Lecture 42 3.2 Speech Tokenizers - Part 2

Lecture 43 Code Eg & Ex - Speech Tokenization(ST) Method Comparison + ST with Enhancd Vocab

Lecture 44 3.3 Language Models in SpeechLMs - Part 1

Lecture 45 3.3 Language Models in SpeechLMs - Part 2

Lecture 46 Code Eg & Ex - Transformer-Based Speech Token Prediction + Speech Token Modeling

Lecture 47 3.4 Vocoders in SpeechLMs - Part 1

Lecture 48 3.4 Vocoders in SpeechLMs - Part 2

Lecture 49 Code Eg & Ex 3.4 - Neural Vocoder for Audio Synthesis + Griffin-Lim Algorithm

Section 5: Module 4: Training Methodologies for SpeechLMs

Lecture 50 Introduction to Module 4 - Training Methodologies for SpeechLMs

Lecture 51 4.1 Overview of Training Stages for SpeechLMs - Part 1

Lecture 52 4.1 Overview of Training Stages for SpeechLMs - Part 2

Lecture 53 Code Eg & Ex - Multi-Stage Training for SpeechLM + Comprehensive Trainig Pipline

Lecture 54 4.2 Pre-Training Methodologies for SpeechLMs - Part 1

Lecture 55 4.2 Pre-Training Methodologies for SpeechLMs - Part 2

Lecture 56 Code Eg & Ex - Lightweight SpeechLM Pre-Training + Advanced Decoding Strategies

Lecture 57 4.3 Instruction-Tuning for Speech Language Models (SpeechLMs) - Part 1

Lecture 58 4.3 Instruction-Tuning for Speech Language Models (SpeechLMs) - Part 2

Lecture 59 Codes 4.2- PEFT of Wav2Vec2 with LoRA + Instruction-Based Speech Recog Tuning

Lecture 60 4.4 Post-Alignment Techniques for Speech Language Models (SpeechLMs) - Part 1

Lecture 61 4.4 Post-Alignment Techniques for Speech Language Models (SpeechLMs) - Part 2

Lecture 62 Codes 4.4 - Real-World SpeechLM Deployment with Post-Alignment Techniques

Section 6: Module 5: Capabilities and Applications of SpeechLMs in Detail

Lecture 63 Introduction to Module 5 - Capabilities and Applications of SpeechLMs in Detail

Lecture 64 5.1 Capabilities and Applications of SpeechLMs: Semantic-Related Tasks - Part 1

Lecture 65 5.1 Capabilities and Applications of SpeechLMs: Semantic-Related Tasks - Part 2

Lecture 66 Codes 5.1 - Whisper ASR Word-Level Timestamp + Zero-Shot Voice Cloning YourTTS

Lecture 67 5.2 Capabilities and Applications of SpeechLMs: Speaker-Related Tasks - Part 1

Lecture 68 5.2 Capabilities and Applications of SpeechLMs: Speaker-Related Tasks - Part 2

Lecture 69 Codes 5.2 - Speaker Verification with ECAPA-TDNN Embeddings + Voice Cloning

Lecture 70 5.3 Paralinguistic Applications of SpeechLMs - Part 1

Lecture 71 5.3 Paralinguistic Applications of SpeechLMs - Part 2

Lecture 72 Codes 5.3 - Speech Emotion Recognition + Prosody-Controlled Speech Synthesis

Lecture 73 5.4 Advanced Voice Interaction with SpeechLMs - Part 1

Lecture 74 5.4 Advanced Voice Interaction with SpeechLMs - Part 2

Lecture 75 Codes 5.4 -RT ASR w/ VAD & Interp. Handling + Turn-Taking Predn. in Conversation

Section 7: Module 6: Evaluation Metrics and Benchmarking of SpeechLMs

Lecture 76 Introduction to Module 6 - Evaluation Metrics and Benchmarking of SpeechLMs

Lecture 77 6.1 Common Evaluation metrics for SpeechLMs - Part 1

Lecture 78 6.1 Common Evaluation metrics for SpeechLMs - Part 2

Lecture 79 Codes 6.1 - Comprehensive ASR Evaluation + TTS Quality Evaluation Framework

Lecture 80 6.2 Evaluating and Benchmarking Speech Language Models (SpeechLMs) -Part 1

Lecture 81 6.2 Evaluating and Benchmarking Speech Language Models (SpeechLMs) -Part 2

Lecture 82 6.2 Evaluating and Benchmarking Speech Language Models (SpeechLMs) -Part 3

Lecture 83 Codes 6.2 - ASR w/ Emotin Recognition + TTS/VC Eval w/ Acoustic Feature Analys

Lecture 84 6.3 Benchmarking Datasets for Speech Language Models (SpeechLMs) - Part 1

Lecture 85 6.3 Benchmarking Datasets for Speech Language Models (SpeechLMs) - Part 2

Lecture 86 Codes 6.3 - Custom ASR + Secure TTS Benchmarkng Framewk w/ SpeechT5 and Pyannote

Lecture 87 6.4 Comparing SpeechLMs w/ Traditional ASR, TTS, and Translation System - Part 1

Lecture 88 6.4 Comparing SpeechLMs w/ Traditional ASR, TTS, and Translation System - Part 2

Lecture 89 Codes 6.4 Comparing SpeechLM vs Traditional ASR System + Emotion Preservation

Section 8: Module 7: Challenges and Future Directions in SpeechLM Research

Lecture 90 Introduction to Module 7 - Challenges and Future Directions in SpeechLM Research

Lecture 91 7.1 Understanding Component Choices in Speech Language Models - Part 1

Lecture 92 7.1 Understanding Component Choices in Speech Language Models - Part 2

Lecture 93 Codes 7.1 - Comparing Speech Feature Extractor + Vocoder Comparison Framework

Lecture 94 7.2 End-to-End Training of Speech Language Models - Part 1

Lecture 95 7.2 End-to-End Training of Speech Language Models - Part 2

Lecture 96 Codes 7.2 - End-to-End Speech Recognition Training + Lite Tacotron TTS Training

Lecture 97 7.3 Scaling Speech Language Models to Larger Sizes and Datasets - Part 1

Lecture 98 7.3 Scaling Speech Language Models to Larger Sizes and Datasets - Part 2

Lecture 99 Codes 7.3 - Scalable Speech Recog Training + Dataset caching, dynamic Bucketing

Lecture 100 7.4 Improving Modeling of Paralinguistic Information in SpeechLMs - Part 1

Lecture 101 7.4 Improving Modeling of Paralinguistic Information in SpeechLMs - Part 2

Lecture 102 Codes 7.2 - Emotion Recog w/ HuBERT Model + Prosody-Control Synthesis FastPitch

Lecture 103 7.5 Handling Low-Resource Languages for Speech Language Models - Part 1

Lecture 104 7.5 Handling Low-Resource Languages for Speech Language Models - Part 2

Lecture 105 Codes 7.5 - Fine-Tuning XLS-R for ASR + Emotion Classification with SpecAugment

Lecture 106 7.6 Developing Real-Time and Duplex SpeechLMs - Part 1

Lecture 107 7.6 Developing Real-Time and Duplex SpeechLMs - Part 2

Lecture 108 Codes 7.6 Streaming ASR w/ Causal Transformer Low-Latency + VAD for Barge-In Sys

Lecture 109 7.7 Addressing Safety and Ethical Concerns in SpeechLMs - Part 1

Lecture 110 7.7 Addressing Safety and Ethical Concerns in SpeechLMs - Part 2

Lecture 111 Codes 7.7 Bias Eval ASR Accent Fairness + TTS Moderation with Toxicity Filterng

This course is for aspiring AI developers, data scientists, and tech enthusiasts eager to pioneer the future of voice AI with Speech Language Models.,Perfect for beginners with basic Python and ML skills, as well as intermediate learners aiming to build advanced applications like real-time speech recognition, emotion-aware voice assistants, and speech translation.,Unlock the power of end-to-end speech processing for cutting-edge careers in AI!