SkillHack › Career guides › Voice AI Engineer
How to become an Voice AI Engineer
Speech recognition, text-to-speech, voice agents, and real-time audio. Build AI you can talk to.
- 5phases in the roadmap
- 15topics to work through
- 15graded practice questions
- Freeno payment, ever
The Voice AI Engineer roadmap
Each topic below carries a study note, real reading, and practice questions in the app. A topic unlocks when you pass the one before it, so the order is the path, so you are never guessing what to learn next.
Phase 1Voice AI foundations
Speech and audio basics
Speech is a sampled waveform, and sample rate and bandwidth are load-bearing: a model trained on 16 kHz wideband audio degrades badly on 8 kHz mu-law telephony.
Automatic speech recognition
ASR maps audio to text; word error rate (WER) is the standard metric but is highly sensitive to text normalization (casing, punctuation, numbers, contractions) and to the reference set.
Text-to-speech
Neural TTS has a text front-end (normalization: expanding currency, numbers, dates, and symbols into spoken words), an acoustic model, and a vocoder.
Phase 2Voice AI pipelines
Streaming ASR
Streaming ASR emits interim (partial) hypotheses that update as audio arrives, then a stabilized final result per utterance.
Latency
Conversational latency is the time from end-of-speech to first spoken audio, and it is a sum of stages: ASR finalization, LLM time-to-first-token, and TTS first-audio.
Endpointing and VAD
Voice activity detection finds speech vs non-speech; endpointing decides when the user has finished a turn.
Phase 3Voice AI conversational
Voice agents
A production voice agent is best built as a modular pipeline: streaming ASR to a dialog/LLM policy with tools to streaming TTS, with clear interfaces at each stage.
Barge-in and turn-taking
Barge-in lets callers interrupt the agent mid-speech.
Grounding
Grounding is establishing shared understanding in dialog.
Phase 4Voice AI quality
Evaluating voice systems
WER is a component metric, not a measure of whether the agent achieves the caller's goal.
Noise robustness
Models trained on clean audio degrade on busy streets, cars, and far-field rooms.
Accents and fairness
A strong aggregate WER can hide large disparities across accents, dialects, and demographic groups.
Phase 5Voice AI in production
Real-time serving
Real-time voice latency is created by serializing full-utterance ASR, full LLM response, and full TTS synthesis.
Cost and latency
Using one large LLM for every turn wastes cost and latency when most turns are simple.
Monitoring
Voice agents regress without a deploy when upstream models, networks, carriers, or traffic mix shift.
Reading for this path
The primary sources behind the topics above, all free to read.
- Deepgram: audio format and sampling basics
- NVIDIA Riva: ASR overview and pipeline
- Whisper: robust speech recognition (Radford et al.)
- Deepgram: measuring and understanding WER
- ElevenLabs: text-to-speech and normalization docs
- NVIDIA Riva: text-to-speech overview
- Deepgram: getting started with live streaming audio
- Graves, Sequence Transduction with Recurrent Neural Networks (RNN-T)
- LiveKit Agents: building low-latency voice agents
- Silero VAD: enterprise-grade voice activity detector
- LiveKit Agents: turn detection and endpointing
- LiveKit Agents: voice agent architecture and quickstart
- NVIDIA Riva: building conversational AI applications
- LiveKit Agents: workflows, tool calls, and confirmations
Coming from another job?
Most people on this path arrived from somewhere else: engineering, analysis, testing, product, support, compliance, design. Onboarding asks what you do today and builds a short starter run out of the gaps, so you begin from what you already know rather than from chapter one. See how it works.
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Start the Voice AI Engineer path for free
The full study notes, the reading, and the practice questions behind every topic above are in the app. Answer one tonight and you have started.
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