---
title: "\"My name is... my name is...\": A Linguistic Map for Building and Debugging Voice Agents"
category: "talks"
date: "2026-06-29"
time: "3:20pm-3:40pm"
track: "Voice & Realtime AI"
room: "Track 6"
speakers: ["Midam Kim"]
sourceLabels: ["Official conference schedule", "Public YouTube metadata"]
scheduleTrack: "Voice & Realtime AI"
scheduleRoom: "Track 6"
scheduleLabels: ["Voice & Realtime AI", "Track 6", "session", "confirmed"]
---
# "My name is... my name is...": A Linguistic Map for Building and Debugging Voice Agents

## Conference Context
- Date/time: 2026-06-29 · 3:20pm-3:40pm
- Track/room: Voice & Realtime AI · Track 6
- Speaker(s): Midam Kim
- Session type/status: session · confirmed

- Track: Voice & Realtime AI
- Room: Track 6
- Session type: session
- Status: confirmed

## Session Description
Every voice AI engineer has heard it: a caller repeating their name three times, getting more frustrated with each attempt. The logs look clean. Confidence scores look fine. Linguistics can help solving the mystery. By the end of this talk, you'll have a diagnostic framework for the failures that slip past standard metrics, a way to turn "the agent just didn't get it" into concrete, debuggable failure modes. The framework maps three levels of linguistic structure (sounds, words, and interactions) against the two dimensions every voice agent engineer already works in: what we hear (speech recognition) and what we speak (speech synthesis). That 3×2 grid surfaces problems your current tooling can't see, including: 1. Why your user cannot make your system understand their name 2. Why a single well-intentioned vocabulary hint can cause catastrophic drops in a non-English language 3. Why a transcript that's "cumulatively correct" can still ruin the user experience Drawing on examples from production multilingual voice AI work, I'll show where linguistic expertise connects to the engineering decisions you're already making and where it reveals failure modes that confidence scores will never warn you about. Who this is for: Voice AI engineers, ML practitioners on Voice AI pipelines, and anyone who's watched clean logs while their agent quietly fails real users.

## Media Evidence
No related AI Engineer channel video found yet.

## Evidence Graph
This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.

### Media Signals
No linked video, transcript, or slide source has been attached yet.

### Agent Reading Notes
Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence.

## Transcript Status
No official session recording transcript was found by exact title match on the AI Engineer YouTube channel during this run.

## People
- [[midam-kim]]

## Notes
- Pending transcript synthesis when an official recording or confirmed matching video is available.

## Synthesis
### Synthesized Breakdown
# "My name is... my name is...": A Linguistic Map for Building and Debugging Voice Agents ## Conference Context - Date/time: 2026-06-29 · 3:20pm-3:40pm - Track/room: Voice & Realtime AI · Track 6 - Speaker(s): Midam Kim - Session type/status: session · confirmed - Track: Voice & Realtime AI - Room: Track 6 - Session type: session - Status: confirmed ## Session Description Every voice AI engineer has heard it: a caller repeating their name three times, getting more frustrated with each attempt. The logs look clean. Confidence scores look fine.

### Speaker And Company Context
- [[midam-kim|Midam Kim]] — ML Engineer at [[servicenow|ServiceNow]].

### Topics Covered
- Topic links are pending transcript-backed classification.

### Derived Links And Source Material

### Novel Concepts / Clever Methods
- No highlighted novel concept has been detected yet.

### Evidence Boundary
This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.