Formant Synthesis, Concatenative Synthesis & Statistical Methods for TTS

Learn about traditional text-to-speech techniques before the rise of neural networks in 2016. Explore formant synthesis, concatenative synthesis, and statistical parametric (HMM-based) synthesis—the methods that paved the way for modern neural TTS. This is video 5 in The Monster Text-to-Speech and Voice Cloning Course, a lecture series designed to give you a deep understanding of state-of-the-art concepts in speech synthesis. 🎯 KEY TOPICS: - The evolution of speech synthesis before deep learning - How formant synthesis modeled the vocal tract - How concatenative synthesis stitched recorded speech units - The rise of HMM-based (parametric) synthesis - Why pre-neural voices sounded robotic or over-smoothed - How these classic methods paved the way for neural TTS CONSULTING: 🚀 AI Music + Audio Consulting: https://valeriovelardoadvisor.com/ 📩 Get my AI Music content in your inbox for free: https://valeriovelardo.substack.com/ COURSE MATERIALS + DISCUSSION: - GitHub Repository: https://github.com/musikalkemist/tts-voicecloning-course - Join The Sound of AI Slack Community: https://valeriovelardo.com/the-sound-of-ai-community/ (#tts-course channel) Content: 0:00 Intro 4:05 Formant synthesis 7:41 Formant: Pros and cons 12:18 Concatenative synthesis 13:41 Diphone concatenation 15:10 Unit selection 25:20 Concat: Pros and cons 27:55 Statistical parametric synthesis (HMM) 38:57 HMM-based TTS: Pros and cons 42:32 Comparing traditional TTS