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!!