Speech Analytics in 2026: How AI Reviews Calls, Service Comparison and Prices

What speech analytics is

Speech analytics is a technology that turns call recordings into decision-ready data: it transcribes calls, separates operator and customer speech, scores each conversation against a checklist, detects key phrases, objections, and emotions, and measures the impact on conversion. It answers not "how many calls were there" (that's PBX statistics) but "what happened inside those calls and what to do about it."

The classic pipeline: call recording → speech recognition (transcription with speaker separation) → text analysis (checklists, sentiment, topics) → a report for the manager. The difference between system generations sits in the middle link: older solutions search the text for predefined words, newer ones understand the meaning of the whole conversation.

Why it matters: humans review 1–5% of calls

A head of sales physically manages to listen to 5–10 calls a week — that's 1–5% of the team's conversations. The other 95% is a blind spot: broken scripts, unhandled objections, "I'll call you back" promises with no callback, and customers who silently left for a competitor. Spot-checking doesn't show the picture — it shows random frames from the movie.

Speech analytics removes that limit: every conversation gets analyzed, not a sample. In practice this delivers three things:

• Quality control without manual listening — every call scored against a checklist: greeting, needs discovery, objection handling, next step.
• Loss reasons — which objections repeat, at what stage customers drop off, which phrasings kill the deal.
• Actionable signals — a negative conversation, a competitor mention, a promise with no CRM task — surface the same day, not in a quarterly report.

Dictionaries vs LLMs: the market's main shift

Until 2024 almost all speech analytics ran on dictionaries: the system searched transcripts for preset phrases ("expensive," "I'll think about it," a competitor's name) and counted hits. Fast and cheap, but a dictionary doesn't understand context: it can't tell "affordable" from "expensive," reads a sarcastic "well, thanks" as gratitude, and simply misses an objection phrased in a non-standard way.

In 2025–2026 the market is moving en masse to LLM analysis: a large language model reads the transcript the way a human does and answers meaning-level questions — "did the manager handle the objection," "why did the customer decline," "what was agreed." Large players build hybrids (ML classification for the stream + LLM for meaning), newcomers go LLM-native from day one. The practical takeaway for a buyer in 2026: the "dictionary or LLM" question matters more than the per-minute price — on complex conversations a dictionary system will draw a beautiful stop-word dashboard and miss the point.

Speech analytics services in 2026: comparison and prices

Based on public pricing as of July 2026; most services price by volume — treat the figures as a reference, not an offer.

The enterprise segment (CRT SmartLogger, BSS, Naumen, MTS and T-Bank solutions) is left out of the table: entry starts at 100–500 thousand RUB/month with months-long deployment — a different market.

The fourth class: an AI agent instead of a dashboard

Every service in the table ends its work the same way — with a dashboard. The system parsed the calls, computed the metrics, drew the chart; then the manager reads the report, finds the problem, and assigns the tasks — personally. The analytics exist; the action doesn't.

Sam Reshu works differently — it is an agent, and call analysis is built into a working loop, not a storefront:

• Hundreds of calls in one pass — with parallel subagents. You say "review the sales team's calls for the month"; the agent pulls recordings from your telephony (from Mango Office — transcripts in batches of up to 500 conversations per request), splits the array into chunks, and hands them to parallel subagents. Each analyzes its batch against your checklist; the lead agent merges the results into one picture. A month of a team's conversations in one request, not a week of exports.
• Analysis quality is verified, not assumed. Every subagent's result is checked against the task's criteria by an independent verification pass; if it doesn't hold up, the analysis goes for a retry. This guards against the main disease of LLM analytics — a beautiful but fabricated conclusion.
• The policy is stated in words and runs on a schedule. "Every morning at 9:00, review yesterday's calls: checklist scores, top objections, which manager lost a customer — and send it to Telegram." That's a standard recurring task: schedule, time zone, delivery to Telegram, email, or the in-app feed. No speech-analytics add-on purchased from your telecom operator? Not a problem: the agent downloads the recordings and transcribes them itself.
• Analysis is carried through to action. A detected objection becomes a task for the manager, an agreement becomes a CRM deal, a series of calls becomes a summary spreadsheet. How a transcript turns into action is covered in detail in AI-powered call analytics, and the operational side — missed calls and SL — in the guide how to connect AI to Mango Office.

Sam Reshu is the only service in the table where bulk call analysis is run by parallel subagents with quality verification, and the analysis policy is configured in plain language as a recurring task.

Honest downsides

If all you need is contact-center metrics — silence share, script adherence, per-group SL — a specialized dictionary system or your telephony's module will be cheaper and more familiar: they ship ready-made supervisor dashboards. If call volume is low (a dozen a day), speech analytics as a class is overkill — the manager can listen to everything personally. An agent pays off where calls are many and the analysis must end in actions — tasks, deals, scheduled reports. And you do need to describe the policy in words once: that takes longer than switching on a module with a fixed checklist, but the checklist ends up being yours, not the vendor's.

Checklist: how to choose speech analytics

1. Dictionary or LLM? Ask how the system will find an objection phrased in a non-standard way. A dictionary system won't; meaning-level conversation control requires LLM analysis.
2. Dashboard or action? If after the report you still assign manager tasks yourself, only the collection is automated — not the work. An agent carries the analysis through to the task, the deal, and the report on its own.
3. All calls or a sample? Check whether the system handles your full stream and what that does to price at your volume: at 4–8 RUB/min, a month of a team's conversations can cost as much as a salary.
4. Whose checklist? The vendor's fixed "28 parameters" — or your criteria stated in words? Every company's sales script is its own.
5. Telephony lock-in. Mango/UIS/Calltouch modules only work with "their" calls. Make sure the solution can pull recordings from your PBX — or transcribe uploaded files itself.

Frequently asked questions

What is speech analytics in simple terms? It's a system that listens to call recordings instead of the manager: it transcribes conversations, scores managers against a checklist, and finds objections and reasons for lost customers — across 100% of calls, not the 1–5% a human manages to review.

How much does speech analytics cost in 2026? By public pricing — from 0.6 to 8 RUB per minute of conversation, or from 750–1,100 RUB/month for a telephony module. Dictionary systems are cheaper, LLM analysis costs more; enterprise solutions start at 100 thousand RUB/month. Price it against your monthly minutes — the gap between services reaches tenfold.

How is speech analytics different from transcription? Transcription turns audio into text and stops. Speech analytics goes further: it scores the conversation, finds objections and script violations, computes metrics. An AI agent takes the next step too — it turns conclusions into actions: tasks, CRM deals, recurring reports.

Can all calls be analyzed rather than a sample? Yes — that's the point. A manager physically reviews 1–5% of conversations; the system covers 100%. Sam Reshu handles large volumes with parallel subagents: hundreds of calls are split into batches, each analyzed separately against your checklist, and the results are merged into one picture with quality verification of every analysis.

How do I deploy speech analytics without a developer? Pick a ready-made service, not an API construction kit. Telephony modules switch on in a day but come with fixed checklists. With an agent, setup means connecting your PBX with an API key from its dashboard plus a policy in plain words: "every morning, review yesterday's calls against these criteria and send the report to Telegram." There is nothing to program.

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Last updated: July 2026.

Sources: public pricing and product pages of Mango Office (PBX speech analytics), UIS, Calltouch Predict, Speech Analytics, Roistat, SalesAI, Rechka.ai, SaluteSpeech (Sber), and Yandex SpeechSense; Naumen's conversational-AI market estimate (Kommersant, 2025: 11 billion RUB, +30% YoY); public materials on the market's shift from dictionary methods to LLM analysis; MANGO OFFICE VPBX API documentation (call transcripts in batches of up to 500 recordings per request).