Uploaded December 2025 | Updated September 2026, 2 weeks ago
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: valeriovelardoadvisor.com
📩 Get my AI Music content in your inbox for free: valeriovelardo.substack.com
COURSE MATERIALS + DISCUSSION:
- GitHub Repository: github.com/musikalkemist/tts-voicecloning-course
- Join The Sound of AI Slack Community: 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
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: valeriovelardoadvisor.com
📩 Get my AI Music content in your inbox for free: valeriovelardo.substack.com
COURSE MATERIALS + DISCUSSION:
- GitHub Repository: github.com/musikalkemist/tts-voicecloning-course
- Join The Sound of AI Slack Community: 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
![This AI Can Solve 604 Tasks [Paper Analysis of Gato by DeepMind]](https://i.ytimg.com/vi/zO49vZ31xb0/mqdefault.jpg "This AI Can Solve 604 Tasks [Paper Analysis of Gato by DeepMind]
DeepMind published a revolutionary paper 🔥 They introduced Gato, a generalist AI agent that can carry out more than 600 tasks with a single transformer neural architecture. The tasks are varied, from playing Atari games to providing captions to images.
This paper demonstrates that:
📌 Generalist agents can perform reasonably well on many tasks / embodiments / modalities
📌 Generalist agents have the potential to learn new tasks with few data points
📌 By scaling up the parameter size, we can build a general-purpose agent
This work shocked me. I’ve always tackled AI from the perspective of Narrow Intelligence: build a specialised model that does well on a single - quite constrained - task.
👉 Gato paves the way for Artificial General Intelligence (AGI). In so doing, it opens new ethical dilemmas that should at least spark discussions in the AI community.
Since I’ve finished reading this paper, I can’t stop asking a question: is it ethical to push this research line given the grave dangers which may come with quasi-AGI agents?
Would you like to learn more? Check my last video, where I provide a breakdown of the paper, and analyse its ethical implications.
A Generalist Agent by DeepMind:
https://www.deepmind.com/publications/a-generalist-agent
Interested in hiring me as a consultant/freelancer?
https://valeriovelardo.com/
Join The Sound Of AI Slack community:
https://valeriovelardo.com/the-sound-of-ai-community/
Connect with Valerio on Linkedin:
https://www.linkedin.com/in/valeriovelardo
Follow Valerio on Facebook:
https://www.facebook.com/TheSoundOfAI
Follow Valerio on Twitter:
https://twitter.com/musikalkemist
Content:
0:00 Intro
1:11 General vs Narrow intellicence
3:06 Research hypotheses
4:25 Idea to approach AGI
8:23 Benefits of single network for many tasks
10:04 Datasets used
11:59 Data preparation
18:24 Model architecture
20:31 Training
22:48 Loss function
27:25 Recognising a task
30:50 Inference
33:37 How does the model perform?
39:00 Scale analysis
40:18 Can the model tackle unseen tasks?
44:14 Key discoveries
47:40 Ethical implications")
