🎙️ Voice AI & Conversational Agents
Learn Voice AI & Conversational Agents on AI4AI — short, hands-on lessons with live AI runs, at three reading levels (beginner to expert). Free to start.
What you'll learn
- Voice AI Today: Speech, Synthesis, and Real-Time Conversation (7 min) — Voice AI systems today follow one of two architectures. The classic pipeline chains three components: automatic speech …
- Speech Recognition That Works: Accuracy, Latency, and Noisy Audio (7 min) — ⚡ Automatic Speech Recognition (ASR) converts audio waveforms into text. The primary accuracy metric is Word Error Rate…
- Natural-Sounding TTS: Voices, Emotion, and Streaming Audio (7 min) — Modern neural TTS models synthesize speech that is perceptually close to human recordings. Rather than stitching togeth…
- Turn-Taking, Interruptions, and Barge-In in Voice Agents (8 min) — Turn-taking is how a voice agent decides when the user has finished speaking and when it should respond. The core tool …
- Realtime Voice APIs: Low-Latency Pipelines (8 min) — Classic voice pipelines chain three separate services: speech-to-text (STT) converts audio to a transcript, an LLM gene…
- Designing Voice UX: Prompts, Fallbacks, and Accessibility (7 min) — Designing voice UX requires rethinking both system prompts and response shape. Unlike text, voice output is linear — li…
Lessons
Voice AI Today: Speech, Synthesis, and Real-Time Conversation
Voice AI systems today follow one of two architectures. The classic pipeline chains three components: automatic speech recognition (ASR/STT) converts audio waveforms to text, a large language model generates a text reply, and a text-to-speech (TTS) engine synthesizes audio back …
Speech Recognition That Works: Accuracy, Latency, and Noisy Audio
⚡ Automatic Speech Recognition (ASR) converts audio waveforms into text. The primary accuracy metric is Word Error Rate (WER): substitutions plus deletions plus insertions, divided by total reference words. Leading models — Whisper large-v3, Deepgram Nova-3, AssemblyAI Universal…
Natural-Sounding TTS: Voices, Emotion, and Streaming Audio
Modern neural TTS models synthesize speech that is perceptually close to human recordings. Rather than stitching together pre-recorded clips (the older approach), they generate audio waveforms end-to-end, capturing natural prosody — the rhythm, stress, and intonation that make s…
Turn-Taking, Interruptions, and Barge-In in Voice Agents
Turn-taking is how a voice agent decides when the user has finished speaking and when it should respond. The core tool is a Voice Activity Detector (VAD) — a lightweight model or signal-processing algorithm that outputs a binary 'speech' vs. 'silence' label in real time. A simpl…
Realtime Voice APIs: Low-Latency Pipelines
Classic voice pipelines chain three separate services: speech-to-text (STT) converts audio to a transcript, an LLM generates a reply, and text-to-speech (TTS) converts that reply back to audio. Each hop adds latency — typically 300–600 ms for STT, 400–1500 ms to the LLM's first …
Designing Voice UX: Prompts, Fallbacks, and Accessibility
Designing voice UX requires rethinking both system prompts and response shape. Unlike text, voice output is linear — listeners cannot skim, scroll, or re-read, so responses must be concise (under 30 words per turn for most use cases), front-load the key information, and eliminat…