Turkish speech to text for business means using AI to convert spoken Turkish, whether recorded or live, into written text. Businesses use it for meeting minutes, call recordings, field service reports and dictation. Good results in Turkish depend as much on microphones, background noise, industry vocabulary and compliance with Türkiye's data protection law (KVKK) as on the model itself.
Where does spoken information go in your company?
Much of what a business knows is never written down; it is said. A decision in the weekly production meeting, a promise made to a customer on the phone, what a technician saw at a breakdown, a sales rep's visit notes: most of it ends up in a recording nobody listens to, or only in someone's memory. Minutes often take hours to write, end up incomplete and later spark the "but we agreed this" argument. The meeting-side solution is covered in AI meeting notes and summaries.
Speech to text makes that information searchable, shareable and usable. Once the words are text, they can be summarised, classified by topic and mined for tasks and dates. That second stage belongs to Turkish natural language processing; large-scale use in contact centres is covered separately in call centre speech analytics.
The cost of doing nothing: recordings, but no information
AI that handles speech is still rare in business. According to Eurostat's data on AI use in enterprises, in 2025 speech recognition, machine learning for data analysis, workflow automation and image recognition were each used by only 3.78% to 7.22% of EU enterprises with ten or more employees. Technologies analysing written text were the most common, at 11.75%.
The picture among Turkish e-commerce businesses is similar. In a survey of 781 e-commerce businesses in the Ministry of Trade's E-Commerce Outlook Report 2025, only 16.8% of businesses used AI for product search or ordering through voice assistants. Speech is our most natural way of communicating, yet it is one of the last sources to become business data.
Collecting recordings without processing them carries risk too. Audio is personal data, and recording without justification can have legal consequences:
In decision 2023/2007 of 30 November 2023, Türkiye's Personal Data Protection Board ruled that an employer had no legitimate interest in recording audio through a workplace security camera, video alone being sufficient for security; the employer was fined and ordered to destroy the audio recordings. (KVKK decision summary 2023/2007)
So the right question is not "should we record everything?" but "which conversations are we recording and transcribing, and why?" The current rules on camera and audio recording are summarised in CCTV recording and KVKK.
Where does speech to text pay off?
| Use | What you gain | What to watch |
|---|---|---|
| Meeting minutes | Decisions and action lists within minutes | Speaker separation, people talking over each other |
| Call recordings | Every call searchable and scoreable | Low audio quality of phone lines |
| Field and service reports | Technicians leave spoken reports instead of typing | Wind, machine noise, gloved hands |
| Dictation (legal, medical, engineering) | Long documents without a keyboard | Specialist terms, special category data |
| Voice notes | A WhatsApp voice note handled like a written request | Short, noisy, accented clips |
| Voice commands in warehouses and plants | Hands and eyes stay on the job | Limited command set, instant response |
The customer voice-note scenario is covered in our WhatsApp business chatbot guide, and warehouse use in voice picking in the warehouse.
What drives accuracy in Turkish speech recognition?
Accuracy is usually measured as word error rate (WER): wrong, missing and extra words as a share of all words. In Turkish a single suffix error ("gönderdik", "we sent", transcribed as "gönderdi", "he sent") counts the whole word as wrong, so character error rate is worth tracking as well. The main factors are:
- Audio source. A meeting-room microphone, a phone line and a lapel mic give very different quality. Phone recordings are narrow-band and harder.
- Noise and overlapping speech. Factories, construction sites and crowded meetings are where models struggle most.
- Industry terms and names. Product codes, customer names, drug and material names are missing from a general model's vocabulary; a custom vocabulary or fine-tuning is needed.
- Accent and speed. Speakers from different regions and fast talkers must be represented in the test data.
- Batch or live? When a recording is processed afterwards, the model sees the full context; for live captions and voice commands, speed comes first.
Cloud or on-premise?
Where you run the speech-to-text model is as important a decision as accuracy:
| Criterion | Cloud service | On-premise model |
|---|---|---|
| Speed to start | Fast; open an account and use it | Needs servers and installation |
| Data location | Often abroad | On your own infrastructure |
| Customisation | Limited vocabulary support | Fine-tuning and custom vocabulary possible |
| Cost structure | Pay per minute used | Mainly hardware and maintenance |
| Suitable recordings | General meetings, low sensitivity | Legal, medical, HR, customer calls |
For many organisations a hybrid set-up makes sense: non-sensitive content is processed in the cloud, while customer and employee conversations stay in-house. Which recordings fall into which class should be set out in a written rule before go-live. How hospitals handle special-category records such as patient notes is covered in AI in hospital operations.
How to set up Turkish speech recognition, step by step
- Write down the use and the purpose. Which conversations, for what purpose, read by whom? If there is no clear purpose, there should be no recording.
- Put the legal framework in place. Inform speakers, set a retention period and restrict access by role. Using voice to identify people may count as biometric data, which carries stricter conditions. For the wider process, see KVKK compliance steps.
- Build a test set from real recordings. Have someone who knows the work transcribe a few hours of audio from your own environment. Models are compared against it.
- Choose where it runs. Cloud services start quickly; for sensitive recordings the model can run on your own servers. Sending recordings to a service abroad is a cross-border data transfer, so the conditions in Article 9 of the KVKK (an adequacy decision, appropriate safeguards or limited exceptions) must also be assessed.
- Add a custom vocabulary and measure. Load product, customer and technical term lists and check the accuracy gain on the test set.
- Connect the text to the work. Minutes should land in the right folder with a summary, service reports on the work order, call summaries in the CRM. In the field, it makes sense to link this to field service management software. How question-answering over an archive works is explained in RAG and enterprise LLMs.
How we do this at Digital Bridge
- Discovery and requirements analysis. We review which conversations are recorded, your recording infrastructure, audio quality and where the text needs to go. You receive a written proposal setting out scope, phases and cost.
- Pilot. We measure models on a test set built from your own recordings and show what a custom vocabulary adds. In our voice recognition and call analysis service, recordings are transcribed with a model tuned for Turkish speech, with speakers separated; batch or live processing can be chosen.
- Integration. Through AI integration we connect the text and its summary to your ERP, CRM, service or document system. So that meeting and call archives can answer questions such as "what did we promise this customer last month?", we make transcripts searchable with an enterprise LLM assistant.
- Data protection. Our data protection compliance consultancy prepares privacy notices, retention and deletion periods and the cross-border transfer assessment.
Minutes and transcripts can be stored in department or project folders in SmartFiles, part of our Smart360 family. SmartFiles analyses a text file the moment it is uploaded and produces a report with Summary, Key Points, Structure and Notable Details sections; users can ask questions of an entire folder, and permissions are granted separately per person or department.
Next step
Start with a single use, such as the weekly management meeting or the service team's daily reports. Collect a few sample recordings and note how minutes are produced from them today. Then contact us and we will measure accuracy on your own recordings together.
For other applications, read where to start with AI in business and browse our Artificial Intelligence hub.