𝗩𝗼𝗶𝗰𝗲 𝗔𝗜: 𝗧𝗧𝗦 - 𝗚𝗶𝘃𝗶𝗻𝗴 𝗬𝗼𝘂𝗿 𝗔𝗜 𝗮 𝗩𝗼𝗶𝗰𝗲

We've covered how Voice AI listens (ASR), understands (NLU), decides (Dialog Management), remembers (Context), and writes (NLG).

Now for the final piece: 🔊 Making it speak.

That's TTS - Text-to-Speech.

𝗧𝗵𝗲 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻:
Input: "Great news! Your flight to Paris is confirmed."
Output: 〰️〰️〰️ (audio waveform).

𝗧𝗵𝗲 𝗧𝗧𝗦 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲:
1️⃣ 𝗧𝗲𝘅𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀
• "How to pronounce this?"
• Normalization ($50 → "fifty dollars")
• Grapheme-to-phoneme conversion
• Homograph resolution (read vs read)
2️⃣ 𝗣𝗿𝗼𝘀𝗼𝗱𝘆 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻
• How should it sound?
• Pitch contour (intonation)
• Duration (speed)
• Stress & emphasis
• Pauses
3️⃣ 𝗔𝗰𝗼𝘂𝘀𝘁𝗶𝗰 𝗠𝗼𝗱𝗲𝗹
• Generate mel spectrogram.
• Tacotron 2, FastSpeech 2, VITS.
• Maps phonemes → audio features.
4️⃣ 𝗩𝗼𝗰𝗼𝗱𝗲𝗿
• Convert to audio waveform.
• HiFi-GAN, WaveGlow, WaveNet.
• Spectrogram → actual audio.

🎯 And that closes the loop:
Listen → Think → Speak

That’s the full Voice AI pipeline.

Thanks for following along - next, I'll likely recap the full system and share a few real-world failure modes that make or break Voice AI in production. More coming soon. Keep building!!

Cheers!!