AI Speech Analytics Development for Call Analysis: STT, NLP, Compliance

Developing AI speech analytics: where call analysis breaks

Recordings in the contact center pile up for years: the archive grows, retention periods are observed, but there is no way to make sense of this volume.

Call quality control rests on manual listening — a supervisor can evaluate a few dozen dialogues per shift, while the remaining thousands remain without evaluation and without a single insight.

The cost of this approach is not immediately visible. Incidents — operator rudeness, promising an impossible deadline, skipping a mandatory phrase — surface only after a complaint, when it is too late to correct.

Reviewing dialogues turns into looking for a needle in a haystack: the needed fragment is first searched for in the telephony export, then manually listened to again.

A separate problem is fragmentation. Recordings are in one system, transcripts are in spreadsheets, evaluations are in the supervisor's personal notes, and it is impossible to bring this together into a single picture.

Speech analytics in this form answers neither the question about the reasons for contacts nor the question of where a specific team is falling short.

Developing AI speech analytics begins with call analysis ceasing to be a manual operation.

Speech recognition in the contact center works like a conveyor: the recording automatically enters processing, is split by participant roles, and is converted to text taking terms and names into account. Then rules and models work on the transcript — from mandatory phrases to signs of operator burnout.

Below we break down how this is addressed: from access to recordings and terminology dictionaries to reports that let a manager see dynamics by team and contact topics.

Implementation effect: recognition quality and failure analysis

We measure the implementation effect with measurements, not feelings: before the start, we take baseline values from the archive; after putting it into operation, we repeat on the same slice.

This shows where the system truly relieves the load on the quality control service and where scenarios and dictionaries need further tuning.

• Archive review coverage. Manual listening covered a few percent of recordings — the rest remained unchecked. After implementation, recognition and automatic scenario-based review cover the entire traffic, including night shifts and contractor lines.
• Speech recognition accuracy. We calculate it on a labeled sample — the open Common Voice corpus plus a sample of the customer's recordings. We break the metric down by line type: on a noisy channel and on compressed connections, quality is noticeably lower than on a reference studio recording.
• Conversation search speed. Search across the call archive runs by phrase, number, operator, and period — the needed episode is found in seconds instead of hours of manual listening.
• Share of automatically detected violations. The absence of a mandatory notice about call recording, interrupting the customer, and confrontational wording are flagged by the system. A specialist arrives at a ready incident rather than searching for it manually.

We look separately at the load on the quality control service: it decreases not by cutting checks, but because routine selection of recordings is taken off people's hands. The freed-up time goes to disputed cases and operator training.

Call processing modes: streaming, batch, hybrid

Streaming recognition accepts audio as it arrives: the conversation is analyzed even before the operator hangs up. This mode is needed where the result affects the current dialogue — operator prompts, triggering quality rules, reaction to stop words during the conversation.

The price is strict requirements for channel stability and a compromise on accuracy for noisy recordings.

Batch processing of recordings works with finished files: audio first goes into storage, then undergoes normalization, channel segmentation, and full recognition.

Accuracy is higher — fewer compression artifacts interfere and there is the context of the whole conversation — but the review delay is measured in hours, and during a call such a result is useless.

A hybrid scheme removes this contradiction: a lightweight streaming pass provides an operational picture and metadata, while a batch pass on the open Whisper model refines the transcript and labeling for reporting. The discrepancy between passes is also an indicator of sound problems on the telephony side.

Choosing a mode is a choice between review delay and transcription quality; the hybrid buys both but requires mature operations.

Development stages: from recording audit to system handover

The work order is structured so that weak spots in dictionaries and rules surface during setup rather than in production, when managers are already using the reports.

Each subsequent stage relies on the previous stage's artifact, so reordering steps usually means redoing the labeling.

1. Archive and channel audit. We review recordings from all sources: telephony, contact center, voice recorders, meeting recordings. We look at channel separation, noise share, dropouts, codecs, and dialogue length. The output is a map of sources with the share of recordings suitable for automatic review and a list of what will have to be cleaned at the input.
2. Flow diagram and processing rules. We fix where export, storage, review, and the reporting layer run. Separately, we agree on anonymization, retention periods, and role-based access — a requirement that is cheaper to build in from the start than to rework the storage after the fact.
3. Dictionary setup. Terms, product names, abbreviations, operator slang, phonetic pronunciation variants. Dictionaries cover what the general model hears incorrectly: without them, tariff and service names are recognized as a random set of words.
4. Sample labeling. We manually label several hundred dialogues and get a reference: roles, topics, key events, violations. We stratify the sample by channels, topics, and connection quality; otherwise the quality assessment will be inflated.
5. Speech analytics implementation. We set up role separation, detectors and tags, and rules for checking scripts and prohibited wording. We select trigger thresholds so that reports do not drown in false positives.
6. Review quality check. We run the system on a holdout sample and look at recognition accuracy, correctness of role identification, and tag completeness and accuracy. We attribute each error to its source: model, dictionary, labeling, or rule.
7. Training and handover. We provide documentation, a dictionary update policy, instructions for analysts, and a quality monitoring scheme. The customer's team then edits dictionaries and thresholds independently — without us.

What we hand over at the output: dictionaries, labeling, access policies

A working pipeline without documentation is a black box: when engineers leave, no one knows why a word ended up in a category, who has the right to listen to a recording, and what to do when accuracy drops.

That is why, along with the system, we hand over a kit that lets you operate, check, and develop it on your own.

• Terminology dictionaries and labeling rules — industry vocabulary, synonyms, abbreviations, an exclusion list; they are replenished from the corpus of your recordings rather than rewritten from scratch with every change in speech.
• Data flow diagrams — where a recording appears, where it is stored, where it is deleted, which copies remain; this diagram then passes security review without friction.
• Role matrix and access policies — who hears audio, who sees only text, who works with reports; access to a recording is not granted "just in case."
• Recording anonymization — personal data is cut out or masked before storage, analysts receive already cleaned text, and the source lives under separate restricted permission.
• Quality control policies — how often recognition accuracy is measured, on which sample, and who rechecks manually; it is fixed what drop is considered degradation.
• System documentation — architecture, configuration parameters, instructions for operators and administrators, and the procedure for updating models and dictionaries.
• Change log and rollback procedure — the dictionary version is tied to the review result, so you can return to the previous one and compare the output on the same recording.

After that, the system lives without us: your analyst updates the dictionaries, your security service grants access, and we remain on change support — through pre-described extension points, not via messenger chat.

Case study: how call review reduced quality control time

The speech analytics case study began with a measurement: the customer is a service company with a contact center of several dozen operators and a flow of 2–3 thousand calls per day.

A quality control department of three people listened to 40–50 dialogues per week — about 2% of traffic. One check took 20–25 minutes: listen to the conversation in full, compare it with the script, record the violation, and prepare a conclusion.

No one reviewed the remaining recordings, so conflicts were learned about from complaints, not from their own call sample.

The implementation example was built around continuous transcription: audio went into speech recognition on the customer's own environment — the Whisper engine was deployed inside their infrastructure so recordings did not leave the perimeter.

Then the text was parsed into roles, utterances, and semantic blocks, on top of which rules worked: trigger words, long pauses, interruptions, skipping mandatory script phrases, and mentions of competitors.

What changed

Implementation results were calculated using the same metrics before and after.

Review coverage grew from 2% to 100% of calls: every dialogue receives a score and labels such as "dispute," "refusal to pay," and "policy violation." The operator opens not a random recording but a filtered sample — check time is reduced to 4–6 minutes instead of 20–25, because the transcript, timecodes, and violation highlighting are already prepared.

Incident response time decreased from several days to a couple of hours: critical words raise a call in the queue on the same day, while the customer is still in dialogue with the company.

The load on the department did not increase — only disputed cases are reviewed manually, and quality reports are generated automatically across the entire base, not from a weekly slice.

How speech review integrates with existing telephony

We connect to the place where the recording already appears — we do not touch the existing telephony.

There are two methods: retrieve finished files from storage on a schedule or when a call closes, or accept audio as a stream through an intermediary receiver service if real-time review is needed. A mixed option is often used: urgent lines via stream, the rest in batch.

The conversation recording remains in your telephony; we do not duplicate it. Recordings are transferred together with metadata: call ID, direction, number, operator, time. Without linkage, review loses meaning — there is a transcript, but it is unclear which call it belongs to. We take metadata from the call log on the same schedule and link it by identifier.

If the telephony cannot send recordings outside, we install a receiver service: it sends the file when the call ends, the service places it in a queue and passes it on. A repeated send does not create a duplicate — reception is idempotent by call identifier.

Different call sources — multiple PBXs, providers, contact center sites — are brought to a unified form: containers and codecs, number of channels, and sampling rate are normalized, and corrupted and empty sections are discarded.

Then a single index over all audio storages, and search runs across all sources at once, not each one separately.

Raw recordings remain with you: only what is needed for review goes here, with a defined retention and deletion policy, and access is granted to a service account with minimal rights.

Technical questions: accuracy, languages, recording storage

Engineers and department heads ask the same thing: what will happen to accuracy on a poor connection, how many languages can realistically be supported, how long to keep the archive, and who will get access to recordings.

Below are the answers we give before the start: they determine the architecture, the scope of additional training, and the timeline of the first production flow.

How many languages can be supported, and what about mixed speech?

We take Russian and English immediately, as well as language switching within a single phrase. We cover industry vocabulary, names, and surnames with dictionaries and hints: this removes most errors without full additional training on a labeled corpus.

How deep an archive is needed, and how should recordings be stored?

Audio and transcripts live separately: text with metrics is cheaper to store and faster to search, while audio follows policy (retention period, cold class, auto-deletion).

It is often enough to keep in full only fragments with triggered control rules; the rest — for a limited term.

Who gets access to recordings, and how?

Personal and payment data are masked at the transcription stage, access is granted by role and written to a log. The environment can be deployed on the customer's side — recordings do not leave the perimeter, and data privacy remains under your control.

Let's discuss the task: what is needed to estimate call review

An estimate for speech analytics implementation rests not on the volume of recordings but on how clearly the sources, search criteria, and destinations for results are described. Below are the inputs we request before calculation. Some are covered on a call, some arrive as an export.

• Channels and volume. Where recordings come from — telephony, messengers, in-person meetings — how many hours of conversations per month and how the flow is growing. This is how we calculate the load and choose the processing scheme.
• Archive format. Mono or stereo, whether both participants are audible, whether there is extraneous noise, and how history is stored. From this — the scope of preprocessing and expected recognition accuracy.
• What we look for in conversations. Scripts and operator evaluation criteria, stop words, product and competitor names, disputed wording. The list turns into rules and dictionaries.
• Regulatory framework. Requirements for personal data storage, retention periods, and access, whether processing needs to be limited to your environment.
• Receiving systems. Where to send results — to CRM, a report storage, or an internal dashboard, and which roles and rights managers and the quality control service need.
• Success criterion. How you will know the implementation worked: share of reviewed calls, incident response speed, and sample coverage for review.

Once we have the answers, we return with an estimate and a work plan: stages, scope, what is covered on your side and what on ours. A ready-made technical specification is not required — describe the task in your own words, and we will work through the rest together.