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Česká verze

Why Czech Presents Unique Challenges for Speech-to-Text — and How to Handle Them

· 6 min read ·

Automatic transcription was developed in an Anglophone world, and English suits it well. Czech is a different case: a highly inflected language with rich morphology, free word order, and a comparatively small pool of available training data. This article explains what Czech specifically demands from a transcription algorithm — and why general-purpose tools fail sooner than you might expect.

Inflected vs. Analytical Language — a Different Kind of Problem

To be clear about what we're dealing with: English and Czech are typologically distinct languages, and this difference has a direct impact on how hard transcription is.

An analytical language like English expresses grammatical relationships through word order and auxiliary words. Word forms change minimally. "The dog bites the man" — position determines who bit whom.

An inflected language like Czech encodes grammatical relationships through suffixes. "Pes kousl muže" and "Muže kousl pes" say the same thing, because case endings determine the role — not word order. For the transcription algorithm this means: the same noun appears in the recording in many different forms, each with a different acoustic fingerprint. The word "pes" (dog) sounds different from "psa," "psu," "pse," "psem," or "psovi." These are all the same word in different grammatical cases.

English has typically two forms for nouns (dog / dogs). Czech can have fourteen or more (seven cases times singular and plural, plus animacy). Each of these forms must be independently recognized by the model as part of the correct word — and with limited training data, that variety causes problems.

Diacritics — a Small Mark, a Big Problem

Diacritics in Czech are not just a spelling convention. They change words into different lexemes with different meanings:

In speech, these pairs are acoustically nearly indistinguishable. Vowel length depends on speaking rate and individual delivery. The algorithm must infer the correct form from context — and if its language model is trained primarily on English without diacritics, that context is missing.

There are two approaches to handling diacritics. Models trained directly on text with diacritics have a better foundation — but they need a sufficiently large training set. Models trained without diacritics add them in post-processing based on dictionary lookup and context. The second path is less reliable and produces higher error rates, especially for low-frequency words.

The practical impact: "byl jsem na rade" vs. "byl jsem na radě" — one is grammatically correct, the other is nonsense. A transcription system that cannot handle this produces text requiring substantial manual correction.

Free Word Order — a Trap for Language Models

Czech allows sentence elements to be rearranged according to communicative emphasis and stylistic intent. "Jana koupila chleba," "Chleba koupila Jana," and "Koupila Jana chleba" are all grammatically correct sentences saying the same thing — with different emphasis.

A language model predicts which word most likely follows the previous one. If it was trained predominantly on English data, it assigns the highest probability to a fixed subject-verb-object order. In Czech, this expectation holds less often. The model may then prefer a "less probable" — but grammatically correct — variant with lower confidence. As a result, sentences with non-standard (but correct) word order are transcribed less accurately.

The consequence for users: transcription of emphatic or stylistically marked sentences will show higher error rates than transcription of sentences with neutral word order.

Training Data Volume — Uneven Playing Field

Transcription models are only as good as their training data. And here Czech is at a significant disadvantage.

English speech is represented in open training datasets at the scale of thousands of hours of validated recordings (LibriSpeech contains 960 hours of read English; Common Voice for English offers even more). Czech speech in Mozilla Common Voice reaches considerably lower volumes.

LINDAT/CLARIAH-CZ, the Czech repository of linguistic data at Charles University, curates academic collections of spoken Czech at https://lindat.mff.cuni.cz. These datasets are valuable for research, but their size and diversity still lag behind English equivalents.

The direct consequence: a model trained on a smaller and less varied dataset generalizes less well. Unfamiliar speakers, regional pronunciation, specialized terminology, or informal speech — all push it into less-explored regions of its statistical space, where error rates climb.

What Actually Helps — Practical Solutions for Czech Transcription

Understanding the problems is useful only when it leads to practical solutions. What genuinely works for Czech transcription?

Models specifically trained or fine-tuned on Czech. Research groups at ÚFAL MFF UK (Charles University) and ZČU in Pilsen have developed transcription models for Czech with results comparable to or better than general-purpose models. Commercial providers such as Google and Deepgram offer Czech language support — the quality of their performance depends on the volume of Czech data in their proprietary training sets.

Custom terminology configuration. If you are processing recordings from a specific domain — medicine, law, IT, internal company jargon — you can supply the transcription system with a list of terms and phrases. The system will favor these terms when uncertain. Czech Transcription System supports this via a CLI parameter and integration with the merge layer, which applies terminology when combining results.

Multi-model approach. One model may fail on a particular grammatical form or expression; another model may handle it better. Combining results from multiple models reduces overall error rates, because the blind spots of individual models only partially overlap. The ensemble approach is discussed in detail in A13.

High-quality recording. The cleaner the audio, the more context the algorithm has available for choosing the correct diacritical form, case, and word order. A good recording compensates for some model shortcomings. How to prepare a recording for the best result is covered in A12.

Conclusion

Czech is not English with accent marks. For a transcription algorithm, it represents an entirely different linguistic system — morphologically richer, with freer word order, its own diacritical system, and a smaller pool of available training data.

The good news: these challenges can be overcome. Models specialized for Czech, terminology configuration, multi-model approaches, and high-quality recordings are paths to better transcription quality. The bad news: a generic tool without these measures will not handle Czech speech recordings adequately — and knowing this before you choose a tool saves disappointment and time.

Sources

  1. Mozilla Common Voice — Czech dataset. https://commonvoice.mozilla.org
  2. LINDAT/CLARIAH-CZ — Czech linguistic data and corpora. https://lindat.mff.cuni.cz
  3. Psutka, J. et al. — research on Czech speech recognition, University of West Bohemia, Pilsen.
  4. Panayotov, V. et al. (2015). LibriSpeech: An ASR corpus based on public domain audio books. ICASSP 2015. [doi:10.1109/ICASSP.2015.7178964]