The Custom Fix for Generic Speech to Text Failures in Regional Call Centres

Speech-to-text technology has come on leaps and bounds in recent years. Accuracy rates appear high, costs have fallen and adoption is widespread. 

Yet in regional call centres, especially those in regulated and technical industries, generic speech-to-text models routinely fail.

The fragility of generic models

Most commercial speech-to-text systems are trained on large, diverse datasets that are designed to generalise across accents, industries and context.

This generalisation has many positives, but is also precisely the problem for regional call centres that operate strong local accents and industry-specific vocabulary.

Generic models often struggle with these intricate details. They don’t often result in total failure, but instead in persistent, low-level inaccuracy.

Small error rates create large costs

An error rate of 12 to 15 percent is often acceptable within consumer applications; but not within operational environments.

Each speech-to-text transcription error creates downstream friction:

• Agents having to correct transcription errors
• Quality assurance teams reviewing calls
• Compliance teams facing audit risks
• Automated analytics become unreliable

In industries such as finance or healthcare, even a minor transcription error can be disastrous. 

Distorted meaning, compromised records or regulatory issues are just a handful of the potential impacts.

The cost is hard to quantify; manifesting itself through lost hours, rework and operational drag.

Accents, jargon and the limits of scale

A generic model trained broadly on US and UK English will struggle to distinguish between regional pronunciations, local place names and industry shorthand. The model is not wrong; it just hasn’t been trained for the task. 

This limitation becomes more pronounced in sectors with technical language, where a simple misrecognition can change the meaning entirely. Within healthcare, medical professionals cannot afford to mix up ileum vs ilium, hyperkalemia vs hypokalemia or apraxia vs aphasia. 

Mistakes could prove fatal.

Domain adaptation: The technical solution

Requiring a higher level of accuracy does not require abandoning automation. Far from it. 

Instead, it requires automation specialisation - the creation and adoption of custom speech-to-text models that truly understand the conversation. 

To do so will require acoustic model fine tuning, which adapts the system to how people in specific regions speak, and language model adaption, which trains the system on industry specification terminology and contextual phrasing. 

The effect of this specialism can be profound.

The operational dividend

Reducing transcription errors does more than just improve the accuracy metrics, instead it:

• Cuts manual review time
• Improves agent productivity
• Enhances compliance
• Enables reliable downstream analytics

Over time these gains will compound. What appears as higher upfront investment delivers ongoing operational savings across the board.

Make the most of NetGeist NLP

NetGeist delivers speech-to-text infrastructure; not just plug-and-play tools.

Our approach is to develop STT models tailored specifically to your requirements. 

Rather than trying to force your operations to adapt to generic AI, NetGeist create the tools that adapt the AI to your operation.

No project is too big… our goal is to develop customized NLP solutions that would fit the concept of your company. 

From virtual assistance to information gathering or financial advice, receive insightful input that would boost the efficiency of your workflow. Let us simplify your textual tasks with a unique solution, tailored specifically just for you.

Generic speech-to-text models work well enough to deploy, but not well enough to become a trusted part of your workflow. For regional call centres where precision is non-negotiable, can you afford to risk using generic tools?