What Word Error Rate Misses on Real Calls

A speech-to-text model's word error rate on clean test clips is a poor guide to how it will handle real phone conversations, so buyers should measure accuracy on audio that sounds like their own calls.

When Does a Talking Avatar Feel Live?

Users forgive a talking avatar for looking like a cartoon, but not for lips that drift from the audio or replies that arrive too late, so teams adding faces to voice agents should treat timing as the main design problem.

Judge the Voice Agent on Handoff and Resolution, Not the Demo

A buyer can judge a voice agent by placing a real call and listening for whether it stays inside approved facts, when it must stop, and what the person receives.

Functional Efficiency: Beyond Recognition Accuracy and Task Completion

Why conversational AI needs a measure of what users accomplish and what they must endure to accomplish it.

Why Voice Search Fails First on Local Modifiers

Voice systems can understand the words in a local query and still fail to resolve what the place, service area or location modifier actually means.

When Low Word Error Rate Still Breaks a Voice Product

Word error rate remains useful for measuring transcript quality, but production voice systems care more about critical-value recovery and end-to-end task success.

Voice AI Is a Workflow Problem Now

As voice models get good enough, the bottleneck moves from the model to the operating layer around it: the inputs, the workflow, the human-in-the-loop, and the discipline of rollout.

The Proper Noun Problem in Speech Recognition

A fluent transcript can still fail the words that carry identity and meaning, so ASR evaluation needs entity-level metrics alongside overall word error rate.

The Most Boring Voice AI Deployment Is Answering Your Phone

While the industry's attention is on reasoning models and agentic workflows, the voice AI use case with the clearest, most measurable return today is picking up the phone when a small business can't.

The Voice Can Sound Right, and the Video Can Still Be Wrong

A believable voice is only one part of successful localization; the translation, speakers, timing, captions, and publishing details must also work together in the finished video.

Why Better Client Tracking Starts With Better Capture of Spoken Clinical Interactions

Modern AI-powered speech recognition and audio capture tools improve clinical documentation accuracy, streamline client tracking, reduce provider burnout, and enhance patient care.

From Large Language Models to Conversational Awareness

Why enterprise voice AI must learn to understand human interaction

Why Voice AI’s Next Big Challenge Isn’t Accuracy. It’s Relationship Design.

Modern voice AI can't continue to follow the same patterns that held the speech industry back for decades.

Goodhart’s Law Is Eating Your Voice AI Rollout

Here are some common voice AI metrics and where they fail. (Featured on SmartCustomerService.com.)

Voice AI: The Dos, the Don’ts, and What’s Next [Featured on DestinationCRM]

Bridging Speech and Hearing: How Audiology Advances Are Powering Next-Gen Voice Recognition

Advances in audiology are teaching machines to listen and hear similarly to humans, which can increase the quality of voice recognition and other auditory technology.

Why Voice AI Is the Next Frontier in Customer Conversations

Conversational AI is now mature enough to support real, human-sounding voice interaction. (Featured on SmartCustomerService.com.)

Conversation Analytics: A Necessity for AI-Enabled Customer Service

These solutions are a foundational component for contact centers of the future. (Featured on DestinationCRM.com.)

Scaling Trust, Earning Customer Love: The Reality of AI Voice Agents

What it really takes to build an AI voice agent that customers trust and on which teams can rely.

AI vs. AI: How Smart Voice Authentication Builds Customer Trust in the Deepfake Era

Authentic voices and the systems that protect them are the real measure of smart customer service. (Featured on SmartCustomerService.com.)