Testing Voice AI: A Practical Way to Catch What Matters
Voice bots are showing up everywhere, from customer service lines and delivery confirmations to telehealth check-ins and insurance claim follow-ups. But building one that works is only half the job. The real challenge is making sure it keeps working in the practical applications, across real users, real environments, and real conversations.
And this is where most teams get stuck.
What makes testing so tricky? Why do bugs slip through even when there’s a checklist? And more importantly, how can teams design a process that actually keeps up?
Let’s discuss what makes voice AI testing so different and how to approach it more pragmatically.
Why testing voice bots is so difficult
If you’ve tested a mobile app or website, you might expect something similar here. You input something, check the output, mark it as pass or fail. But voice AI doesn’t behave like traditional software. It’s not deterministic. It doesn’t always give the same answer to the same question. And it doesn’t just process inputs, it interprets them.
Under the hood, a voice interaction runs through a pipeline:
ASR (Automatic Speech Recognition): Turns spoken words into text
LLM (Large Language Model): Analyzes the transcript, figures out intent, and generates a response
TTS (Text-to-Speech): Converts that response into synthetic speech
Each of these steps introduces risk. And the problems don’t stay neatly contained, they affect all the other steps downstream.
Take a simple user query like: “Can I cancel my order for tomorrow?” If the ASR picks up “Can I cancel my order for tomorrow?”, which it might if the user mumbles or there’s background noise, the LLM may return an answer about medical cancellations or ignore the question entirely.
The TTS might then pronounce that nonsensical response clearly, making the error sound intentional. At that point, the user is confused or frustrated, and the bot has no idea it’s gone off track.
This cascade effect is hard to debug. Did the issue start with mishearing? Misunderstanding? Mis-speaking? You often can’t tell without replaying the whole exchange, and even then, the answer might not be clear-cut.
Now let’s add real-world variables:
Accents and dialects: An Indian-English speaker might say “thirty” and be heard as “thirteen.”
Noise: A car honk or crackling phone line might cut out part of a sentence.
Code-switching: A bilingual user may start in Hindi and switch to English mid-sentence.
Domain-specific language: A health bot must understand drug names like “Metoprolol,” while a bank bot must handle account numbers, transaction codes, or phrases like “block my card.”
Speech quirks: Elderly users may pause often. Younger users might speak fast, use slang, or drop key words.
You don’t get to control these variables, but your QA process has to account for them. And unlike text bots, you can’t just skim transcripts. Voice interactions need to be heard to be understood. That makes the testing process slower, more manual, and more subjective.
Worse, the system itself evolves. Update the prompt, fine-tune the LLM, or change the TTS provider, and you might unintentionally break responses that were working fine last week. Even minor changes can cause unexpected regressions.
Why manual QA doesn’t scale—and what to do instead
Right now, most teams try to catch these failures by manually reviewing calls. That usually means pulling call recordings, listening to each one end to end, and scoring them against a long checklist: Did the bot greet the user properly? Was the data captured? Was the tone natural? Did it handle corrections, interruptions, background noise?
A thorough review of one call typically takes 8 to 10 minutes, especially when testers have to rewind, compare transcripts, cross-check captured data, and evaluate both content accuracy and delivery quality.
Now imagine doing that across just 50 calls. That’s nearly a full day’s work for one person. And that doesn’t account for the variety of scenarios that need testing:
Different languages and dialects
Background noise conditions (traffic, TV, poor cellular connections)
User styles (fast talkers, hesitant speakers, emotion-laden speech)
Domain-specific use cases (insurance claims, prescription refills, customer escalations)
There are also dozens of edge cases: users mixing languages mid-sentence, calls where the bot is interrupted halfway, people responding out of order, or interactions with silence gaps, laughter, or sarcasm. Each of these needs a different lens for evaluation.
But most QA teams don’t have the time or bandwidth to test across that entire matrix. So they fall back on small sample audits, spot-checks, or “happy path” testing. That leaves large swaths of potential failure modes unchecked.
And here’s the problem: critical issues don’t always show up in the obvious places. A compliance violation might appear once in 100 calls. A rare TTS glitch might only happen when a specific word is used. If you’re testing randomly or treating all calls the same, you’re likely to miss the high-impact problems.
That’s where traditional QA falls short. It assumes every call deserves equal attention. But in reality, what’s needed is a framework that prioritizes which calls to inspect closely based on actual risk, issue severity, and likelihood.
A smarter QA framework
Voice AI systems are too complex and too variable for flat, one-size-fits-all testing. Instead of treating every call the same, we use a two-phase strategy that separates low-risk interactions from high-risk ones and helps teams spend their QA time where it actually matters.
Phase 1: Quick Scan
Quick Scan is built for speed and coverage. The goal is to triage, not analyze. You review a large batch of calls (e.g., 50–100) at a glance, spending no more than 1–2 minutes per call. You're looking for signals, not depth.
What to catch at this stage:
Compliance checks: Was consent asked explicitly? Any signs of policy violations, unsafe content, or bias?
Core functionality: Did the bot parse user intent and respond meaningfully?
Hard failures: Was there silence, dropout, or complete ASR or TTS breakdown?
UX anomalies: Was the user confused, repeating themselves, or dropping off abruptly?
Each call is scored on a short set of pass/fail flags, many of which could be semi-automated (e.g., LLM scans for missing consent prompts, profanity, or null outputs). The outcome is a filtered list of ~10–20% of calls that show red flags and need detailed inspection.
Phase 2: Deep Audit
This is where full diagnostic QA takes place. For each flagged call (plus a small sample of clean ones), you run through a detailed checklist that covers:
ASR quality: Was the transcription accurate? Were domain-specific terms recognized?
LLM performance: Was the response coherent, on-topic, and contextually correct?
TTS output: Was the speech natural, correctly pronounced, and paced well?
Dialogue flow: Did the bot recover from interruptions? Did it skip questions or forget prior context?
User experience: Was the interaction smooth, emotionally appropriate, and respectful?
Each issue is categorized by severity (critical, major, or minor) and used to generate a final call score (1 to 5). This allows teams to track quality systematically, prioritize regression fixes, and compare model performance across releases, languages, or deployment environments.
This framework isn’t tied to any specific stack or toolchain. It’s adaptable to any ASR–LLM–TTS pipeline and can be used alongside automated metrics (e.g., latency logs, error codes) to flag review candidates faster.
From framework to metrics
This blog outlines the framework, but the real work is in the details.
We’ve published a working Voice QA Metrics document that breaks down every testable parameter from Phase 2. It includes exactly what to check, how to test it, how to measure outcomes, and how to score call quality consistently.
This isn’t a finished product, it’s a shared resource that we’re actively improving with feedback from teams on the ground. You can leave comments, suggestions, or new test cases directly in the doc.
We’re inviting contributions from anyone involved in the design, development, or deployment of voice bots:
QA leads – What failure modes are hardest to detect today?
Developers – Are there ways to automate parts of this process more effectively?
Conversational designers – Where does nuance or tone break down in real conversations?
Researchers – What edge cases or patterns should this framework capture better?
Product owners – What would make this usable for your teams day-to-day?
If there are any other questions you can help us answer, we’re open to your ideas and suggestions. To contribute examples, ideas, or edge cases that aren’t captured yet, email Santosh at santosh@ekstep.org.
The goal is to build a repeatable, shared QA standard for voice systems across sectors, languages, and tools. Your feedback will help shape what comes next.