Let's dissect it into the things that really count before you deploy: where voice fits within your product, how to achieve real-time performance, how to measure quality, what to build vs buy, and the compliance pitfalls you don't want to stumble into.

Define the job voice that is performing, rather than the feature

Begin with a job description, not a demo. Examples:

• "Book, reschedule, or cancel an appointment in less than 90 seconds, without using hands."
• "Triage support calls, gather the facts, and pass on to a human with a clean summary."
• "Let drivers handle orders without taking their eyes off the road."

Every role has different assumptions. A triage agent can take an additional second to get it right. A driver assistant cannot. A concierge voice in your mobile application can assume quiet environments and a solid network. A phone line bot can't. Map the work to the environment, background noise level, device type, and what the user expects. That dictates your stack from day one.

If you're doing rapid prototyping of voices or narration, an AI voice generator can assist you in testing tone and pacing without committing to talent yet. Leverage it to confirm persona and script rhythm with actual users, then make voice commitments once you're sure of the brand fit.

Latency targets that won't kill the conversation

Voice has a physics issue. Humans detect lag. Two easy rules solve this:

• Sub-second turn response feels natural. Below about a second, most users remain in flow
• For phone and live chat, keep one-way audio <150 ms and round-trip time under a second to allow interruption and back-channels

Achieve those numbers with streaming, not request-response. Stream the mic audio up when the user speaks, stream partial transcripts down to your dialog layer, and stream synthesised speech back as soon as the first words are synthesised. Implement WebRTC or similar real-time transport for browser and app experiences, and bidirectional media streams for calls. Implement barge-in so users can interrupt your assistant in the middle of a sentence, and you actually cut off talking immediately. If you don't, users will yell, repeat, and hang up.

Operationally, you'll monitor p50 and p95 end-to-end latency per turn, along with jitter. Budget each leg: capture, encode, uplink, ASR, policy/LLM, TTS, downlink, playback. If one leg goes haywire, you'll hear about it in the next three.

Real-time architecture fundamentals you can't get around

A production voice loop is like this:

• Capture audio continually from the mic or telephony
• Stream it to ASR with partial results
• Feed partial text to your policy layer. Incremental planning to prevent waiting for the final transcript
• Initiate TTS as soon as you receive a first clause to say, and continue generating as words trickle in
• Enable user barge-in and truncate TTS when they start talking
• Log everything with timestamps to debug later

On mobile or web, WebRTC provides you with media capture, transport, and NAT traversal. On phones, employ your carrier or a telephony provider that supports bidirectional media streams so you can stream audio to and from your computer. You'll want a jitter buffer, silence detection, and click-to-talk cues if your app is running on noisy hardware.

Lastly, instrument your real-time pipeline. Read WebRTC stats, server queue times, ASR and TTS inference times, and playout delays. You can't fix what you can't see.

Quality is not one number. Measure the three that matter

Voice systems thrive or perish by three independent qualities: recognition, reasoning, and rendering.

• Recognition (ASR). Headline measure is Word Error Rate (WER), but don't leave it there. Monitor entity accuracy for names, dates, numbers, and domain jargon. Maintain a lexicon for product SKUs and brand names. Anticipate dialect and accent variation, and test for it explicitly. If your users are from Lagos, Liverpool, and Louisville, your model must demonstrate that it can handle all three. Fine-tuning or biasing vocabularies for key terms will have a quick payback
• Reasoning (your dialog brain). Good transcripts still fail if the agent chooses poorly. For voice, include a "silence tolerance" and "clarification policy." When the user hesitates, do you wait, ask, or summarize and request confirmation? Architects who, ahead of time, don't allow it to drop out of defaults. For safety, set refusal and escalation rules and maintain a quick path to a human
• Rendering (TTS). Naturalness is usually assessed by Mean Opinion Score (MOS). Your users will not view MOS charts; they will experience prosody. Use SSML to manage rate, emphasis, breaks, and say-as for phone numbers, money, and dates. Provide guardrails at maximum speaking rate so your assistant does not rush when you are trying to calm down an upset customer

A simple weekly scorecard will keep you honest:

• ASR WER overall, and by accent, environment, and device
• Entity accuracy on the top 50 domain terms
• Turn latency p50 and p95
• Barge-in success rate
• Task success rate and average handle time
• Drop-off before task completion
• Escalation rate to human and reason codes
• MOS or user satisfaction for voice quality

Bottom line

Voice works when you build for conversation, not speech. Make latency transparent, anchor your agent in your data and policies, grant users agency to interrupt, and be clear about what is synthetic and what you keep. If you value the constraints and the humans behind the mic, voice will open up more of what your product can do, and who it can serve, than a clever demo.