Speech, Multimodal, and Embodied Language
For readers building systems that see, hear, speak, understand documents and video, or connect language to action.
Part of Language AI Handbook. The online chapters are free to read; the PDF edition is yours to keep, with future updates included.
Become a more confident language AI practitioner.
Build the understanding to explain how language models work and make more informed choices about using them. The handbook connects the foundations to the methods behind modern language AI, with explanations you can return to as you learn.
See what you’ll learnWhat you will learn
- Understand Multimodal Models.
- Understand Speech and Audio.
- Understand Omni-Modal and Embodied Language Systems.
Who this volume is for
- Engineers, researchers, students, AI enthusiasts, linguists, product managers, and anyone interested in understanding or building modern language AI systems, from foundational NLP to advanced large language models.
Prerequisites: Assumes transformer and model-family foundations from Volumes 5, 8, and 9.
Volume 17 PDF edition
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Get all-volume access · $199Reader verdict
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out of 5
12 five-star reader reviews
Genuine feedback, with names abbreviated for privacy.
Meet the community“Really appreciate the way you have explained the concepts. Simple to understand, yet builds up knowledge exponentially, all while reinforcing it with examples repeatedly. I've read the last two chapters `74.Self-attention & 75.Q,K,V` and I've to admit, my concepts have never been clearer. You really are doing an amazing job breaking down maths and complex concepts like a story!”
“I would like to thank you for your work. Specifically, the chapter: 'LIME Explainability: Complete Guide to Local Interpretable Model-Agnostic Explanations' was extremely helpful for me in understanding LIME and communicating to colleagues. Your work is inspiring and valuable. Thank you for doing it.”
“I am enjoying reading your writings. I appreciate the effort you put into it. I want to drop a note of thanks. Thank you!”
“Your writing feels like a treasure trove of great material. So, thank you for compiling everything on your website!”
“I found your book Language AI Handbook's explanations of Transformer architectures and production deployment strategies to be exceptionally clear and insightful”
“Thanks so much for establishing such an amazing community. I appreciate the opportunity to be a learner and a contributor to the community.”
“Thank you Michael. It's top!”
“I came across your collection of online handbooks covering quantitative finance, AI, and machine learning. I wanted to reach out and thank you for making such high-quality material openly available. They are incredibly helpful for my CFA Level II review and for brushing up ahead of ML interviews.”
“I came across one of your books, Language AI Handbook, and man, I'm loving it. I've read many books but have never found such a comprehensive book on large models that covers everything from start to finish with such depth.”
“I just came across your website where your books are available for free to read. Thank you so much for making this available to the public. This will help a lot of people.”
“Thanks for putting cool stuff out into the world.”
“I read a few of your blogs on NLP and oh WOW, they really are something, now I exactly know how all of the terms Entropy, Cross-entropy and how it is connected to Perplexity, will definitely read more of your blogs.”
Read all 12 reader reviews
“I would like to thank you for your work. Specifically, the chapter: 'LIME Explainability: Complete Guide to Local Interpretable Model-Agnostic Explanations' was extremely helpful for me in understanding LIME and communicating to colleagues. Your work is inspiring and valuable. Thank you for doing it.”
“Really appreciate the way you have explained the concepts. Simple to understand, yet builds up knowledge exponentially, all while reinforcing it with examples repeatedly. I've read the last two chapters `74.Self-attention & 75.Q,K,V` and I've to admit, my concepts have never been clearer. You really are doing an amazing job breaking down maths and complex concepts like a story!”
“Thanks so much for establishing such an amazing community. I appreciate the opportunity to be a learner and a contributor to the community.”
“I just came across your website where your books are available for free to read. Thank you so much for making this available to the public. This will help a lot of people.”
“I am enjoying reading your writings. I appreciate the effort you put into it. I want to drop a note of thanks. Thank you!”
“Thank you Michael. It's top!”
“Thanks for putting cool stuff out into the world.”
“I found your book Language AI Handbook's explanations of Transformer architectures and production deployment strategies to be exceptionally clear and insightful”
“Your writing feels like a treasure trove of great material. So, thank you for compiling everything on your website!”
“I came across your collection of online handbooks covering quantitative finance, AI, and machine learning. I wanted to reach out and thank you for making such high-quality material openly available. They are incredibly helpful for my CFA Level II review and for brushing up ahead of ML interviews.”
“I read a few of your blogs on NLP and oh WOW, they really are something, now I exactly know how all of the terms Entropy, Cross-entropy and how it is connected to Perplexity, will definitely read more of your blogs.”
“I came across one of your books, Language AI Handbook, and man, I'm loving it. I've read many books but have never found such a comprehensive book on large models that covers everything from start to finish with such depth.”
Exact contents
17 chapters across 3 parts
Every linked chapter is free to read online. 7 further chapters are planned; unlinked titles are still in progress.
Part L: Multimodal Models
- 01Vision Transformer
Covers image patching, patch embeddings, ViT architecture, ViT pre-training.
- 02CLIP
Covers CLIP architecture, CLIP training objective, CLIP zero-shot classification, CLIP embeddings.
- 03Vision Encoders for VLMs
Covers ViT variants for VLMs, SigLIP improvements, image resolution handling, encoder selection.
- 04Vision-Language Projection
Covers linear projection, MLP projection, Q-Former approach, projection training.
- 05LLaVA Architecture
Covers LLaVA design, two-stage training, visual conversation, LLaVA variants.
- 06Flamingo Architecture
Covers cross-attention to images, gated cross-attention, few-shot visual learning, Flamingo training.
- 07Multimodal Training Data
Covers image-text pairs, interleaved documents, visual instruction data, data quality.
- 08Multimodal Evaluation
Covers VQA benchmarks, multimodal understanding benchmarks, multimodal generation evaluation.
- 09Multimodal Foundations
Covers multimodal learning principles, cross-modal alignment, joint embedding spaces, multimodal challenges.
- 10Image Understanding
Covers visual question answering, image captioning, visual grounding, scene understanding.
- 11Image Generation
Covers diffusion models, text-to-image generation, image editing, generation evaluation.
- 12Multimodal Applications
Covers document understanding, medical imaging, video understanding, multimodal reasoning tasks.
Part LI: Speech and Audio
- 01Speech Representations
Covers mel spectrograms, mel filterbanks, feature normalization, audio preprocessing.
- 02Whisper Architecture
Covers Whisper encoder-decoder, multitask training, language tokens, timestamp prediction.
- 03Whisper Training
Covers Whisper training data, weak supervision, multilingual training, Whisper capabilities.
- 04Speech-Language Integration
Covers speech encoder + LLM, audio tokens, speech-to-text-to-LLM vs end-to-end, speech LLM architectures.
- 05Text-to-Speech
Covers TTS architecture overview, vocoder role, TTS quality metrics, neural TTS approaches.
Part LII: Omni-Modal and Embodied Language Systems
- 01Unified Audio-Visual-Language Modeling · In progress
Modality encoders, shared token spaces, fusion, generation, and interference in omni models.
- 02Video and Temporal Reasoning · In progress
Frame sampling, event localization, temporal memory, causal reasoning, and long-video evaluation.
- 03Streaming Multimodal Interaction · In progress
Continuous perception, interruption, turn-taking, proactive responses, latency, and duplex generation.
- 04Document, Diagram, and Interface Understanding · In progress
OCR, layout, charts, diagrams, screenshots, grounding, and visually situated tool use.
- 05Speech-to-Speech and Expressive Voice · In progress
Direct speech interaction, prosody, speaker identity, emotion, latency, and voice-specific safety.
- 06Vision-Language-Action Models · In progress
Grounded language for robotics, action tokenization, policy learning, embodiment, and simulation-to-reality gaps.
- 07Multimodal Safety and Evaluation · In progress
Cross-modal injection, privacy, deepfakes, modality imbalance, interactive benchmarks, and human evaluation.
Choose the edition that fits your next step
Volume 17: Speech, Multimodal, and Embodied Language
540 pages focused on this subject, with future updates included.
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Choose this volumeThe complete Language AI Handbook library
All-volume access with separate volume PDFs and future updates. No combined PDF. Includes future volumes as they become available.
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Get all-volume accessExplore the other volumes
Volume 1 · 570 pages
Language Data and Classical NLP
Text as Data · Classical Text Representations · Distributional Semantics
Volume 2 · 790 pages
Embeddings, Tokenization, and Sequence Labeling
Word Embeddings · Subword Tokenization · Sequence Labeling
Volume 3 · Coming soon
Linguistic Structure and Meaning
Morphology · Syntax · Semantics · Discourse · Dialogue
Volume 4 · 920 pages
Neural NLP and Sequence Models
Neural Network Foundations · Recurrent Neural Networks · Sequence-to-Sequence
Volume 5 · 860 pages
Transformers from First Principles
Self-Attention · Positional Encoding · Transformer Blocks · Transformer Architectures
Volume 6 · 510 pages
Efficient, Long-Context, and Alternative Architectures
Efficient Attention · Long Context
Volume 7 · 760 pages
Pre-training Data and Scaling
Data Curation · Pre-training Objectives · Scaling Laws
Volume 8 · 440 pages
Encoder, Multilingual, and Translation Models
BERT and Variants · Encoder-Decoder Models
Volume 9 · 730 pages
Decoder Language Models and Capabilities
GPT Architecture · Modern Decoder Models · Emergent Capabilities
Volume 10 · 920 pages
Large-Scale Training and Sparse Models
Training Infrastructure · Training Optimization · Mixture of Experts
Volume 11 · 730 pages
Fine-tuning and Efficient Adaptation
Fine-tuning Fundamentals · Parameter-Efficient Fine-tuning · Instruction Tuning
Volume 12 · 510 pages
Alignment, Preferences, and Oversight
Alignment and RLHF
Volume 13 · 220 pages
Reasoning and Verifiable Learning
Reasoning
Volume 14 · 640 pages
Efficient Inference and On-Device Models
Model Compression · Inference Optimization
Volume 15 · 600 pages
Retrieval, Knowledge, and Memory
Retrieval-Augmented Generation · Continual Learning
Volume 16 · 510 pages
Agents and AI Coding Systems
Code Generation · Tool Use and Agents
Volume 18 · 950 pages
Evaluation, Factuality, and Reliability
Evaluation Fundamentals · Benchmark Evaluation · Human and Model Evaluation · Hallucination and Factuality
Volume 19 · 760 pages
Responsible, Secure, and Interpretable Language AI
Bias and Fairness · Interpretability · Safety and Security
Volume 20 · 510 pages
Applications, Production, and Compound AI Systems
LLM Applications · Production Systems
Author and edition details
About the author and this edition
Michael Brenndoerfer
Michael has spent more than a decade working across software engineering, data, AI, and business. He writes to understand difficult ideas more deeply and to share what he learns in a clear, practical way.
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- First edition
- Published
- Last reviewed
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Library access includes every future volume and update. When a volume changes, its updated PDF appears in My books at no extra cost.
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Edition 2026.08.0
Initial volume-library release with 19 written PDFs, a Volume 3 placeholder, and no combined edition.