Generative voice AI has moved far beyond simple text-to-speech — and this course takes you from the physics of sound all the way to building production-grade, agentic voice systems.
Most TTS courses stop at basic vocoders or off-the-shelf APIs. This one goes deeper. You’ll start with the fundamentals of human speech — acoustics, phonetics, and prosody — before diving into the architectures actually powering today’s state-of-the-art voice models: self-supervised representation learning (wav2vec 2.0, HuBERT), neural audio codecs (EnCodec, SoundStream, DAC), and the tokenization strategies that let LLMs “speak.”
From there, you’ll master the two dominant modern paradigms — autoregressive codec-based TTS and latent diffusion / conditional flow matching — understanding exactly when and why each is used in real systems. You’ll also explore unified speech-text models, paralinguistic modeling (laughter, breathing, affect), and zero-shot voice cloning.
By the final module, you’ll understand how to build low-latency, streaming, agentic voice pipelines — the same techniques behind real-time conversational AI agents — covering chunked inference, speculative decoding, WebSocket streaming, and turn-taking.
What you’ll learn:
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The science of speech production and acoustic feature extraction
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How neural audio codecs and semantic tokenization work
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Autoregressive and diffusion/flow-based TTS architectures
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Cross-modal speech-text alignment techniques
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Building low-latency, interruption-aware conversational voice agents
Whether you’re an ML engineer, researcher, or voice-tech founder, this course gives you the complete architectural picture — from tokens to agents.
