ASR quality, usability and data security

Conversation analytics only works if three things are true: the transcription is accurate, the tools are usable by the people who need them, and the data is kept secure. Get any one wrong and the insight collapses — a poor transcript produces poor analysis, an unusable tool sits idle, and a security gap makes the whole thing a liability.

For large enterprises in regulated industries, these are not nice-to-haves. This article looks at each foundation in turn: speech recognition quality, platform usability, and data security.

ASR quality is the base for call analysis

Good analysis is impossible without a good transcription. If the audio quality is poor or the model is weak, everything built on top of it inherits the errors. That is why the automatic speech recognition model matters so much for analysing phone calls.

Feelingstream uses its own ASR models, built specifically for transcribing customer service calls rather than generic speech. Base models exist for a range of languages, and they can be adapted to a customer's own calls to push transcription accuracy higher — so the analysis that follows is trustworthy.

Usability: value for every team, not just data scientists

A powerful platform that only specialists can operate does not deliver value. The aim is to put insight from customer conversations into the hands of many teams across a large organisation — from frontline coaches to process owners — which means the interface has to suit a wide range of users and use cases.

Practical improvements make that real:

• Consistent ways to view and analyse calls, chats, emails and feedback across an omnichannel solution.
• Flexible query options, charts and easy sharing of findings.
• The freedom to inspect a single event, run ad-hoc searches, or do long-term analysis as needed.

A user favourite is the audio player synced to the transcript: agents and coaches can speed up or slow down playback and click through the text to find exactly what they need, reviewing calls for QA in a fraction of the usual time. Alongside the main analytics tool, model-building capabilities let business users classify their conversations without deep technical expertise.

Security: a first-class concern, not an afterthought

Security has been central from the start, and it only matters more with GDPR and emerging AI regulation. Protecting the private information shared in conversations means handling consent, granting access on a need-only basis, and being able to regulate that access by user, event type, channel and time.

Masking personally identifiable information (PII) is a major focus. A combination of named entity recognition (NER), blacklisting and whitelisting, backed by monitoring, allows the value of conversations to be unlocked while mitigating the risk of exposing PII. The anonymisation is done in-house and tailored to each customer, so the pipeline can move from audio to anonymised transcripts and then offer regulated access — no reliance on external anonymisation tools.

Feelingstream holds the ISO/IEC 27001 certification. If security governs your buying decision, the data security in conversation analysis guide goes deeper.

Where these foundations are heading

Large language models and related AI have opened new possibilities, but sensitive customer conversation data calls for caution rather than diving in headfirst. The direction is to bring more automation to business users — faster, smoother service experiences — while keeping the founding principle intact: the user stays in control of what and how they analyse, now with more technological help. As before, these tools support cost, sales and quality use cases in parallel.

Frequently asked questions

Why does ASR quality matter so much?

Because analysis is only as good as the transcript beneath it. A weak model produces errors that flow into every downstream insight, so purpose-built, adaptable ASR is the foundation of reliable conversation analytics.

Who is the platform designed for?

Business users across many teams — coaches, team leads, process and product owners — not just data scientists. The interface and model-building tools are built so non-technical users can analyse conversations themselves.

How is PII handled?

Conversations are anonymised in-house using NER combined with black/whitelisting and monitoring, so PII is masked before analysis. Access is then regulated by user, event type, channel and time.

Is Feelingstream certified?

Yes. Feelingstream holds the ISO/IEC 27001 certification. See data security for the full picture.

Where to go next

• The pillar guide: Automated call-centre quality assurance
• The foundation: Speech-to-text transcription
• Security in depth: Data security
• Trust hub: Data security in conversation analysis

Accurate transcription, usable tools and airtight security — see all three on your own conversations. Book a demo.