Why does my speech-to-text keep messing up names, emails, and numbers in customer calls?

Most teams only notice their speech-to-text is “off” when something critical breaks: a wrong customer name in the CRM, an unreadable email in the ticket, or a contract value that doesn’t match what was actually said on the call. The root cause is rarely your note-taker UI. It’s almost always STT that was never designed to reliably handle names, emails, and numbers in messy, real-world audio.

This FAQ unpacks why this happens technically, what it breaks downstream, and how to fix it without rebuilding your entire stack.

Quick Answer: Your speech-to-text system keeps messing up names, emails, and numbers because most ASR models are trained on generic audio and text, not noisy telephony or entity-heavy conversations. They struggle with accents, 8 kHz call audio, and rare tokens (like names and domains), which leads to entity errors that cascade into broken notes, summaries, and CRM syncs.

---

Frequently Asked Questions

Why does my speech-to-text get names, emails, and numbers wrong so often?

Short Answer: Because most STT models aren’t optimized for entity accuracy on real call audio — they’re trained on generic data, struggle with 8 kHz telephony, and treat names/emails/numbers as low-frequency tokens, so they’re the first to fail.

Expanded Explanation:
In customer calls, the “hard” parts of transcription aren’t filler words; they’re entities: names, company names, emails, phone numbers, addresses, amounts, dates. These tokens are sparse in training data and carry most of the business value. When you combine that with noisy lines, overlapping speakers, accents, and low-bandwidth telephony (SIP, 8 kHz), generic ASR models will default to “closest-sounding common word” instead of the precise entity.

The result is exactly what you’re seeing: “Jon Smitht at Acmee Corp,” unreadable emails, and “eighteenn thousand” instead of a clean numeric value. Those aren’t cosmetic errors. They break your downstream flows — CRM enrichment, ticket routing, contract follow-up — and make users stop trusting your product.

Key Takeaways:

• Generic STT isn’t tuned for entity-heavy customer conversations, especially over telephony.
• Names, emails, and numbers are rare but high-impact tokens, so they fail first when audio or models are stressed.

---

How do I figure out what’s actually causing these transcription errors?

Short Answer: Start by isolating the error types (entity, speaker, timestamp, parse) and correlate them with audio conditions (telephony vs. VoIP, noise, accents, crosstalk) using a small but representative evaluation set.

Expanded Explanation:
“Bad transcript” is too broad to be diagnosable. You need to label what’s going wrong: is the name wrong, the number ambiguous, the speaker misattributed, or the timestamp drifted? In practice, I’ve seen the same STT stack look fine on clean demo audio and completely fall apart on 8 kHz call center traffic. Without a structured evaluation harness, you end up guessing and swapping APIs blindly.

A simple but disciplined process is enough: collect a slice of production calls (with consent), annotate ground-truth for critical entities and speakers, and compute error rates. You’re looking for patterns: does entity error spike on specific languages or carriers? Do numbers fail when spoken fast or over crosstalk? Once you see the pattern, you can decide whether you need a different STT engine, better audio handling, or entity-aware add-ons.

Steps:

1. Collect a representative sample of real calls (telephony, accents, noisy conditions), not clean internal demos.
2. Annotate ground truth for names, emails, numbers, and speakers on 30–100 calls.
3. Measure targeted metrics (entity accuracy, WER on numeric spans, diarization quality) and compare STT vendors or configurations against the same dataset.

---

Is this just an accuracy problem, or do diarization and timestamps matter too?

Short Answer: It’s both — even if the words are right, bad speaker diarization and drifting timestamps will misattach entities to the wrong person or moment, which is just as damaging as misrecognizing them.

Expanded Explanation:
Most teams focus on word error rate (WER), but in customer calls, “who said what and when” is equally critical. If your STT knows the email but assigns it to the wrong speaker, your CRM may attach a personal email to the agent instead of the customer. If timestamps drift by a few seconds, action items extracted later won’t line up with the audio, and QA or compliance teams can’t audit what actually happened.

So you’re fighting three fronts at once: recognition (did we get the text right?), diarization (did we attach it to the right speaker?), and alignment (does it match the audio timeline?). Names, emails, and numbers usually appear at key moments — introductions, qualification, pricing — where diarization and timestamps must be precise. If any pillar fails, your downstream summaries and syncs degrade, even if the raw transcript looks “okay” at a glance.

Comparison Snapshot:

• Option A: Word-only accuracy focus
  • Good-looking transcripts in a text editor.
  • Breaks when you try to map entities to speakers, QA segments, or CRM fields.
• Option B: Accuracy + diarization + timestamps
  • Slightly more complex to evaluate.
  • Holds up in production: reliable “who said what,” aligned clips, auditable entities.
• Best for: Products where transcripts power automation — note-takers, QA tools, agent assist, CRM syncs.

---

How can I actually improve entity accuracy on calls without rebuilding everything?

Short Answer: Switch to an STT backbone that’s built for telephony and entity-heavy conversations, then layer entity-specific capabilities like custom vocabulary and NER on the same API.

Expanded Explanation:
You don’t need to rewrite your note-taker or CRM integration; you need a stronger STT and entity layer behind your existing flows. In practice, what works is one API that gives you: real-time + async transcription, solid diarization, accurate timestamps, and add-ons for entities (names, emails, numbers) — all tuned for noisy, multilingual, 8 kHz audio.

That’s the job Gladia is designed for. Our Solaria-1 engine is benchmarked on conversational speech and telephony-like conditions, not just podcast-quality English. It handles SIP/8 kHz, code-switching, and crosstalk, and exposes entity-focused capabilities — like high-precision named entity recognition (NER), custom vocabulary, and multilingual support — through the same REST/WebSocket surface. You keep your architecture; you swap the backbone that powers it.

What You Need:

• A production-grade STT API that’s telephony-ready (8 kHz, SIP), supports real-time + async, and includes diarization and timestamps by default.
• Entity-aware add-ons on the same API: custom vocabulary, NER for names/emails/numbers, and multilingual handling for mixed-language calls.

---

Strategically, why should I care if a few names or numbers are wrong, as long as the gist is there?

Short Answer: Because those “few” errors are exactly what drive revenue, compliance, and trust — when names, emails, and numbers are unreliable, your summaries, automations, and CRM syncs quietly fail and users blame your product.

Expanded Explanation:
In voice products, you don’t get credit for “roughly correct.” If a summary says “spoke with Jon Smitht at Acmee Corp” and the opportunity is under “John Smith at Acme Corp,” your sales ops or CSM now has to guess which is right. If a contract value is misheard, you either lose revenue or get into a dispute. If a complaint call’s phone number is wrong, ticket routing or compliance tracking breaks.

This is exactly the failure pattern we see: bad STT → minor entity errors → degraded summaries and CRM writes → users lose trust and churn. From a strategy standpoint, tightening entity fidelity is one of the highest-leverage moves you can make. It doesn’t just “improve transcripts”; it stabilizes every downstream workflow and makes your automation credible.

Why It Matters:

• Impact on revenue and operations: Wrong amounts, dates, and contacts directly affect billing, renewals, and follow-up.
• Impact on trust and churn: Once users see obviously wrong names/emails/numbers, they stop relying on your product for critical tasks and quietly churn.

---

Quick Recap

If your speech-to-text keeps messing up names, emails, and numbers in customer calls, you’re bumping into the limits of generic ASR: models trained on the wrong data, under telephony constraints they weren’t optimized for, with no entity-aware layer. The fix isn’t another UI tweak; it’s swapping in an STT backbone that handles noisy, multilingual 8 kHz audio, preserves “who said what and when,” and treats entities as first-class citizens through NER, custom vocabulary, and reliable timestamps. Once that backbone is solid, your notes, summaries, and CRM syncs stop breaking on the basics.

Next Step

Get Started