Speech transcription accuracy in analytics: how errors distort results

Short answer

Call analytics rests on the transcript, and a transcript — speech turned into text — always comes with some error. The most dangerous errors for analytics are misspelled domain terms and product names, misheard numbers (price, date, quantity), lost negation ("can" instead of "can't"), broken language switching and mixed-up speakers. Each distorts a different result: product demand, price objections, intent, the objection map.

The problem is that transcription errors do not show in the report. The number looks convincing; it simply rests on wrong conversations. So every team using call analytics should check transcript quality in its own language and domain.

Why an overall accuracy figure is not enough

Speech recognition systems are usually rated by an overall error rate: how many words are recognised wrongly. But not all words are equal for analytics. Errors in words like "and", "this", "okay" affect no result. An error in a product name, a price or a negation changes a whole field. The overall figure can look good while the 5% of words that matter to you are exactly the ones most often misrecognised.

Error type 1: domain terms and names

Product, model, service and brand names — especially foreign names and abbreviations — are the most often misrecognised words. Result: values in the "product of interest" field are split wrongly, demand for one product is credited to another or falls into "other". Check: search transcripts for your 10 best-selling product names and compare with the real calls.

Error type 2: numbers

Prices, dates, sizes and quantities are often confused in speech: "fifteen" and "fifty", "thirteen" and "thirty". Result: fields such as budget range, the amount in a price objection or a delivery date are filled wrongly. Check number-based fields separately and, where possible, take the number from the system (order, CRM) rather than the conversation.

Error type 3: negation

In Azerbaijani, negation is often a small suffix at the end of the verb — the difference between "I'll take it" and "I won't take it" can be one syllable. In a noisy call that difference is easily lost. Result: the intent field can flip completely — a refusal reads as agreement. Check negation-related results in intent and outcome fields specifically.

Error type 4: language switching

Customers mix Azerbaijani, Russian and English words in one sentence. If the recognition system is set up for one language, words in the other are misspelled or dropped. Result: an objection or product name said in Russian turns into an unreadable word in the transcript. Know the share of mixed-language calls and check them separately.

Error type 5: mixed-up speakers

If the agent's sentence is attributed to the customer in the transcript, analytics counts the agent's words as the customer's view: the scripted "the warranty is 2 years" looks like customer interest in warranties. This happens mainly with mono recordings. More: speaker diarization.

The role of audio quality

• Mobile networks, noisy surroundings, speakerphone mode — all raise the error rate.
• Stereo recording (each side on a separate channel) makes separating speakers much easier.
• Recognition is unreliable on very poor recordings — excluding or flagging them is more honest.
• If the recognition system gives a confidence score per line, flag low-confidence lines separately.

Which error distorts which result

1. 1Product demandTerm and name errors.
2. 2Price objections and budgetNumber errors.
3. 3Intent and outcomeNegation errors.
4. 4The objection mapMixed-up speakers — the agent's words read as the customer's objection.
5. 5Channel and segment comparisonAudio quality — one channel (say, mobile calls) is systematically recognised worse.

How to build a domain glossary

The most practical way to reduce term errors is a domain glossary: a list of product, model, service and competitor names, plus domain-specific words, each with the misspellings that often appear in transcripts. The glossary is used in two places: if the recognition system allows it, it is passed in as hints; and the misspellings are added to analytics fields' examples so AI links them to the right value. Update the glossary along with the product list — when a new model launches, its name belongs in the glossary too.

A checking routine

1. 1Sample20–30 calls a month: different channels, hours, agents, language mixes.
2. 2ListeningCompare each call's key moments (product name, number, negation, objection) with the audio.
3. 3RecordingWhich error type, affecting which field.
4. 4ActionA glossary for domain terms, flagging poor recordings, taking numbers from the system.

Illustrative example

This is an illustrative example. A car service sees "brake disc" enquiries suddenly drop in its call analytics. Checking shows enquiries have not fallen: after switching to a new phone line, audio quality got worse, the term is transcribed in various wrong forms, and AI puts it into "other".

Audio quality is fixed, the most common misspellings are added to the field's examples, and the last two weeks' calls are re-analysed. The "fall in demand" was in fact a measurement error.

Typical mistakes

• Applying a vendor's overall accuracy figure to your own language and domain.
• Never comparing transcripts with the audio.
• Treating a sudden drop in a value as real change without checking.
• Giving poor-quality calls the same weight as good ones.

Limits

• A transcript does not carry tone of voice, intonation or pauses — text shows only the words.
• Error rates vary by language, accent, audio quality and domain; one check does not cover everything.
• No serious decision about an employee should be made from a transcript without checking the audio.

Transcript reliability in Vexvon

Vexvon conversation analytics works with the text stored in conversations and shows the customer's own sentence as evidence next to every result — which makes transcription errors easier to catch, because a suspicious result can be read immediately. Frequently repeated phrases in "other" appear separately: a misrecognised term often surfaces there. Transcribing recorded calls themselves, separating speakers and flagging low-confidence lines are the job of a call analysis tool. More: analytics.

Next step

This month, listen to 20 calls and check how product names, numbers and negations are written in each transcript. The list of most often misrecognised words is your first fix plan. To check AI categories, see AI categorization validation; we can set up the check together in a demo.

Further reading on this topic: insufficient evidence analytics, conversation analytics taxonomy.