Assessing the Accuracy of AI Transcription of Professional Terminology

A transcript may contain almost every ordinary word yet remain unsuitable for teaching a professional topic. Replacing a method name, losing a negation, or confusing a measurement unit is enough. Readers receive a different meaning while retaining a good general impression of the text. Terminology checking therefore needs a separate plan: what counts as a term, how the reference is established, which differences are acceptable, and which errors block further use.

Assess a specific recording within a specific process. A claim on a service's website or someone else's result in another language does not determine your lecture's quality. Below is an author-created protocol for an educational team. It combines ordinary recognition checking with subject analysis but is not a universal certification standard for systems. Every numerical example is hypothetical: the examples explain calculations and decisions; they do not report test results for a particular product or course.

Define the Transcript's Learning Purpose

Finding a passage in a large archive permits one set of limitations; publishing instructions permits another. Record what readers will do with the text: review terms, reproduce a procedure, quote an explanation, or search for a topic. This determines the checks. A word barely affecting search can determine whether a professional action is correct. Do not apply the same tolerance to all material merely because one tool produced it.

Distinguish the automatic draft's quality from that of the edited result. If an editor spent substantial time correcting terms, final accuracy is not the model's intrinsic accuracy. Keep both versions for assessment and describe human work separately. This permits honest answers to different questions: whether the system suits a first pass, how much checking is required, and whether material is ready for students within the defined learning task.

Compile a Subject-Specific List Before Reviewing the Result

List terms from the curriculum, slides, and approved sources. For each, establish its full name, abbreviation, permitted forms, and cases where a similar word denotes another concept. An abbreviation without expansion may be correct in one context and ambiguous in another. Do not build the glossary solely from words the system recognized successfully. That would exclude precisely the difficult words the algorithm replaced or omitted entirely.

The analysis unit may be one word or a phrase. Define it in advance. For a multiword name, establish whether only the complete name counts or its components are assessed separately. If one part distinguishes two methods, lenient word-by-word counting hides a substantive substitution. Also retain a category for terms absent from the initial glossary but confidently established by a subject editor through independent listening.

Prepare a Sample Containing Real Difficulties

Collect excerpts representing your typical conditions: different instructors, speech rates, microphone distances, disciplines, and lesson formats. Include terminology-dense explanations and ordinary speech. Otherwise, assessment answers only a narrow question about artificially selected difficult fragments or creates an overly optimistic impression from easy introductory sentences. Describe sample composition before comparing system results.

Do not call a small convenience sample representative of all Ukrainian lectures. A pilot provides local observations for designing the next check. If the sample contains no student questions, the conclusion does not cover dialogues. If all recordings are studio-made, they do not establish classroom behavior. Keep unstudied conditions alongside the findings so subsequent users understand practical limits and do not extend conclusions without new data.

Create the Reference Independently of the Automatic Draft

Establish the reference from audio, using subject materials to clarify spelling. Ideally, the first editor should not see the recognition output: a plausible system version may bias their hearing. Mark uncertain passages for a second listening. If two editors disagree, seek subject-specific clarification or retain explicit uncertainty. Do not make the most convincing phrase the reference merely because it is grammatical and fits the topic.

The reference does not quietly correct the instructor's knowledge. If the recording contains a slip, preserve it according to the chosen transcription rule. A substantive correction can be added as a separate note. Otherwise, a system accurately reproducing the sound would be penalized for not matching an edited textbook sentence. Connect every difficult decision to a timestamp so a disputed case can be reconsidered without rereading the entire file or relying on the previous editor's memory.

Record Normalization Rules

Before comparison, agree on case, apostrophes, hyphens, numbers, and punctuation. A number written in words and digits can mean the same thing but technically appear as different tokens. Apply the same rule to reference and hypothesis. Also retain unnormalized versions for meaning analysis and readability. Automatic conversion to a shared form simplifies counting but can conceal a difference important for learning material or a professional name.

Do not remove everything preventing a good score. Units, negations, and symbols conveying meaning remain subject to checking. Normalization should remove agreed technical differences rather than consequences of recognition errors. Set a separate rule for ambiguous abbreviations. If the system changed one abbreviation into another, do not reduce them to a common root merely to obtain a match. Manually check several examples before counting to see how the procedure actually works.

Calculate the Overall Word Error Rate

The common WER metric counts substitutions, deletions, and insertions relative to the number of words in the reference. The formula appears, for example, in NIST's materials on recognition evaluation: WER = (substitutions + deletions + insertions) / reference words. Multiply by one hundred for a percentage. Correct sequence alignment is needed first: one omitted unit should not mechanically make every subsequent word incorrect through simple positional comparison of lines.

Hypothetical example: the reference has one hundred words; the system makes four substitutions, two deletions, and one insertion. This yields seven error operations and a WER of seven percent. It does not automatically mean "ninety-three percent of the meaning is correct." The metric counts word differences rather than each difference's importance for professional understanding. Two systems with identical WER can distort terms in entirely different ways. Do not make this metric the sole decision on whether learning instructions can be published.

Add a Separate Analysis of Term Occurrences

Count target terminology occurrences in the reference, how many are conveyed acceptably, and which are substituted or omitted. One word repeated ten times gives ten occurrences but only one term type. Show both views. Otherwise, a frequent easy word can hide the fact that the system never reproduced a rare but central name. Describe the counting method explicitly: this is a team working metric rather than a universally defined accuracy measure for every professional concept.

Separately search for inserted terms absent from the speech. Counting only reference occurrences' correctness can easily miss these additions. For each case, record the time, what was heard, what was produced, and the consequence. A general "terminology error" label is insufficient. Editors need to see whether it is a familiar form of another concept, abbreviation, phonetic substitution, or contextual word completion. This detail helps select the next step: glossary, another recording, manual clarification, or process change.

Check Numbers, Units, and Negations

Professional accuracy does not end with a noun glossary. A number with the wrong unit, omitted condition, or changed sign can alter an explanation's meaning. For recordings involving measurements, also check quantity and unit formatting against subject-specific sources; general SI rules are described by BIPM. This does not mean an editor can reconstruct an unspoken number from formatting rules. The recording or explicit author clarification remains the source of a specific value.

Introduce a category for meaning-critical cases. The instructor defines it according to the material's purpose. A tool name is critical in one lesson, a formula's application condition in another. Do not assign weights after seeing which system wins. Rules agreed in advance make comparison fairer. An unresolved critical case can keep material in draft even with a good overall score. Explain that decision through a specific misunderstanding risk rather than an abstract demand for perfection.

Separate Acoustic and Editorial Problems

A clearly audible word replaced by the system is one situation. Several people being unable to decipher the sound is another. In the latter, repeat generation may produce different plausible versions without new information. Do not choose the most frequent as proof of correctness. Mark the recording limitation and clarify with the author. If a word is intelligible, check context, language settings, and whether the particular tool supports supplying a glossary.

Separate editorial improvement from recognition too. An automatic assistant may hear a term correctly but replace it with an everyday synonym during later "style improvement." Assess at least the raw output and final material. If accuracy declines after editing, replacing the recognition model will not necessarily solve the problem. You may need a prohibition on glossary changes and separate checking of meaning differences between versions prepared by different editorial participants.

Compare Systems on the Same Basis

Use the same files, reference, and comparison rules. Record the date, model, version, and supplied context. If one variant receives a glossary and another does not, call it a comparison of different processes. It can help practical selection but does not prove one algorithm's intrinsic superiority under identical conditions. Do not mix results with and without manual correction in one column labeled "accuracy."

Reserve some recordings for an independent follow-up check. If you repeatedly changed prompts while looking at the same excerpt, its result already reflects adaptation to a familiar example. A new recording shows whether improvements transfer. For a small sample, report observed counts and conditions rather than far-reaching statistical claims. Changing one difficult occurrence can noticeably alter a percentage while grounds for a general conclusion remain limited.

Collect Results in a Working Table

For each excerpt, retain its name, duration, sound conditions, reference-word count, word errors, term occurrences, and unresolved cases. Add human checking time and a suitability decision. Do not replace the log with a long automatic summary lacking concrete examples. A subject specialist must be able to open the disputed passage and repeat the check. A simple table is enough if it preserves the connection between conclusion, text, and original audio.

For example, hypothetical system A handles ordinary speech well but confuses two key names. System B differs more in filler words but preserves these names. Selection depends on learning purpose and checking cost. Do not declare a winner solely by overall percentage. Ask the editor which errors are practical to correct, difficult to notice, or likely to pass unnoticed into student material. This turns measurement into a team decision.

Do Not Confuse Professional Accuracy with Accessibility

Even correct terminology does not guarantee usable text. Paragraphs, clear speaker identification, and descriptions of meaningful sounds or visible actions may be needed where meaning would otherwise be lost. W3C WAI describes transcripts as textual representations of necessary information rather than merely mechanical word lists. A video lecture may need descriptions of what the instructor points to. Approve these additions separately so editorial description is not attributed to spoken speech.

Check search too. If a professional name has an approved abbreviation, readers should understand the connection between forms. Add a glossary where needed, but do not change quotations without labeling. Headings and timestamp links help navigation. Check them after export: good recognition does not prevent transfer errors. Keep these tasks separate in the report so editors see what has been checked against sound and what still needs formatting work.

Define Acceptance for Use and Rechecking

Set thresholds for the specific course and purpose and explain their meaning. Do not borrow an arbitrary number from advertising or another study in a different language. Draft search material can retain explicit limitations; core learning text needs resolved questions of meaning. A general approach to evaluating and controlling generative-system risks appears in the NIST profile, but it sets no universal acceptable percentage for Ukrainian terminology in your discipline.

After a model update or microphone change, repeat checks on control and new fragments. Retain previous results to reveal the nature of changes rather than only the latest number. If a new discipline appears, expand the glossary and sample. Returning to old errors should be a controlled process. Even a successful initial pilot does not remove the need to check material differing in acoustics, terminology, or subsequent team editing.

Transfer Verified Material into the Course

When the WeStudy LMS platform organizes learning, publish the result only after subject acceptance and checking the available delivery method. A glossary, revision date, and way to report inaccuracies can accompany it. This describes a workflow rather than claims automatic WER measurement or built-in recognition of specific terms. Check your environment's technical capabilities separately before planning import, synchronization, or automatic updating of the entire archive.

The WeStudy LMS platform does not change the evidential requirements for an editorial conclusion. The team should know who confirmed a term, against which recording, and for which text version. Students need usable material, while instructors need the ability to correct problems quickly. Quality assessment delivers both: measurable observations about a system and a clear path for preparing learning text. That path's value lies in controlled decisions about specific errors rather than one attractive number on a report cover.

Analyze One Incorrect Term

Consider a hypothetical recording where the system replaced an algorithm's name with an ordinary phrase fitting the sentence well. The editor first establishes the exact time and listens to the phrase with the preceding explanation. They then compare what they heard with the glossary and instructor's material. If sound is clear enough, they correct spelling and label the substitution type. If doubt remains, they give the author both alternatives without suggesting which the team prefers. Clarification remains a recording check rather than approval of a preselected answer.

After establishing the term, search for related cases in the same recording. Do not limit yourself to exact searches for the incorrect phrase: one term may have been rendered several ways. Review repetition contexts and places introducing an abbreviation. Correcting one paragraph does not confirm other occurrences. Keep the example in the control set, but do not make it the only test after the next update. New cases are needed to reveal transfer of improvement rather than merely training the team to recognize one known error among familiar sentences.

Do Not Hide Differences Through Averaging

When combining excerpts, show the overall result and separate groups. A large quantity of easy speech can overwhelm weak performance on a difficult professional section. Word-count weighting answers one question; averaging by excerpt answers another. State how the summary was obtained. A short pilot needs no complex statistical report, but readers must understand what underlies the number. For very small groups, report observed counts rather than imply a stable pattern for a whole discipline or every future instructor.

Retain differences between editors too. One specialist may accept an abbreviated name while another requires its full form. This is not necessarily a system error: the team may have defined the rule insufficiently. Revisit the disputed example, clarify the criterion, and apply it again to every relevant case. Do not change only the row affecting a desired comparison. Consistent criteria matter more than one convenient result. They let a new reviewer understand the decision without attending every previous discussion and reproduce it on another recording.

Keep a Short Limitations Card

Add a card to the findings: what was checked, what was not, which rules applied, and who approved subject-specific clarifications. State whether the set includes several languages, speakers with different pronunciations, and poor-audio fragments. If not, do not add these conditions for persuasiveness. The card helps colleagues decide whether your conclusions suit their task. It also prevents a local observation about one lecture accidentally becoming a general product characterization repeated without the underlying data.

Separately agree who decides when the author does not respond. A low-risk search description can retain a marked gap, while a core instruction may require deferring use of the fragment. Make this choice explicitly. The technical ability to export a file does not establish that every necessary word is known reliably enough for the planned learning use.

Sources

1. NIST: ASR Metrics.

2. W3C WAI: Transcripts.

3. BIPM: SI Brochure.

4. NIST: Generative Artificial Intelligence Profile.