The Hard Parts of Turkish Speech Recognition

Speech recognition vendors advertise a high accuracy figure. That number is usually measured on English, wide band audio and read text rather than on anything resembling a sales call.

Your environment is different on four axes at once: Turkish, telephone quality audio, improvised speech and background noise. All four push accuracy down, and Turkish speech recognition inherits every one of them.

Suffixes keep changing the word

Turkish is agglutinative. A single root arrives in dozens of surface forms, each one a different sequence of sounds the model has to recognise separately.

In English a word usually appears in one or two shapes. In Turkish the same meaning spreads across many, which naturally raises the word error rate before anything else goes wrong.

The practical consequence is about search, not just accuracy. Looking for a plain word in a transcript will not find the sentence where that word carries four suffixes.

Search and rule matching therefore have to work on the root rather than on the surface form, otherwise a correct transcript still fails to trigger the rule that depends on it.

This is the failure teams misdiagnose most often. The transcript is right and the automation is still wrong, so the vendor gets blamed for something the matching layer caused.

Business Turkish is mixed language

Real business speech is not pure Turkish. A typical sentence carries Turkish grammar, English word roots and Turkish suffixes attached to those English roots.

I entered this lead into the CRM, but when will we do the follow-up, should we book the meeting here?

If the model is set to Turkish it spells the English roots wrongly. If it is set to English it loses the structure of the sentence, and both failures corrupt the same transcript differently.

The fix is a custom vocabulary, with those words spelled the way your sector actually writes them. The decision is yours, and consistency matters more than which form you pick.

Consistency matters because every downstream rule is written against one spelling. A vocabulary that alternates between two forms produces reports that quietly undercount the same thing twice.

Numbers are where it hurts most

Numbers are where voice agents make the most errors, and unfortunately they are also where the most important information lives in a franchise conversation.

Investment amount, floor area, phone number: the most critical fields of a franchise call are all numeric, and all of them are the fields recognition handles worst.

When the model gets one wrong the transcript still looks readable while the information inside it is false, which is more dangerous than not capturing it at all.

A missing field announces itself; a wrong number does not. Nobody reviews a field that looks filled in, which is how a transcription error ends up in a contract.

Brand and proper names

The model does not know your brand. It does not know the brands in your sector either, and it confuses person and place names regularly.

The effect is larger than it sounds. A sentence mentioning a competitor will not trigger the competitor mentioned signal if the brand name was transcribed wrongly.

A recognition error turns directly into a lost business signal, which is the kind of failure nobody notices because the transcript reads fine.

• Your own brand and product names
• The main competing brands in your sector
• City and district names that come up often
• Sector specific terms and abbreviations
• Campaign and package names

This list is not written once and forgotten. It grows with every new campaign, and an unmaintained vocabulary becomes useless within a few months.

Who should own the vocabulary

It belongs with whoever names campaigns rather than with engineering. The people who invent new terms are the only ones who know when the list has gone stale.

A quarterly review is usually enough. What is not enough is treating the vocabulary as part of the initial setup, because that is exactly how it stops matching reality.

Phone audio and people talking over each other

A phone line compresses audio into a narrow band and clips the high frequencies, which is exactly the region where several Turkish consonants are distinguished from one another.

The second problem is overlap. In real conversations people interrupt each other, and two voices mixed into a single channel break speaker separation.

When speaker separation breaks, an objection said by the customer can be recorded as something the representative said, which corrupts every downstream signal built on it.

The fix is dual channel recording where the operator supports it. Keeping each side on its own channel turns speaker separation from a guess into a fact.

How to measure accuracy yourself

Measure your own number rather than the vendor's. The method is unglamorous and takes about a day, and it is the only figure that describes your calls.

1. 1.Pick 20 to 30 of your real calls across different representatives and regions
2. 2.Transcribe them by hand; boring, but the only correct reference
3. 3.Compare the model output and compute the word error rate
4. 4.Classify the errors: numbers, brands, suffixes or speaker separation
5. 5.Fix the largest class first, which is usually the custom vocabulary
6. 6.Measure the same 30 recordings again; if nothing improved, what you did did not work

Running that loop twice usually gains more accuracy than switching vendors, and it costs a fraction of a migration.

What this means for the rest of the system

Turkish speech recognition is not a solved problem, but it is a manageable one. Most of what lowers accuracy sits around the setup rather than inside the model.

No vocabulary, a single channel, unverified numbers and nobody measuring: those four explain the majority of complaints we hear about transcript quality.

Recognition delay is also part of response time, because a misheard word makes the agent ask again, as covered in the article on voice agent latency.

Once the transcript is correct the real work starts, which is covered in the article on what a single call reveals.

And whether you own this tuning yourself or inherit it from a vendor is part of the platform decision, covered in the article on building versus buying.