AI is transforming language localisation from translation to real understanding

True language localisation helps agents understand tone, intent and cultural nuance. (Image: Connect)

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In a multilingual region like the European Union, or a multicultural country like South Africa, with its 13 official languages and differing dialects, language localisation is far more than a courtesy or convenience; it’s the bedrock of effective and meaningful customer engagement.

Contact centre operators who can communicate with customers in their preferred language, or switch between native tongues and English, not only build consumer trust, but these capabilities also enhance comprehension and reduce friction across service interactions.

Language localisation is quickly becoming the secret weapon in AI-powered customer experience. It’s what separates systems that merely transcribe from those that truly understand.

Beyond simple translation, true language localisation helps agents understand tone, intent and cultural nuance in the moment to ensure every customer feels understood and valued.

For organisations operating in diverse linguistic environments, customer conversations no longer follow clean textbook language. They weave together dialects, mixing English with vernaculars, while using cultural references and slang, against a backdrop of environmental noise.

A traditional or generic model trained on “standard” English or “neutral” Spanish assumes one size fits all. A single model trained on oceans of generic data is expected to perform equally well on a London help desk, a Mumbai logistics call and a São Paulo street interview. In practice, it doesn’t.

These models are optimised to work well in general, but not in specific situations, making them ill-equipped to navigate a complex and nuanced communication environment. Without a deeper understanding specific to the culture and language of a country or region, these models can quickly lose their footing when they encounter real human speech – messy, musical and deeply local.

They misinterpret regional variations, stumble on acronyms and fail to capture entities like dates or currencies when phrased naturally.

Add industry-specific technical jargon to the mix, and generic models can fall woefully short when operating in multilingual specialised domains, like financial services or healthcare, increasing the risk of misunderstood prompts and frustrating customer experiences.

In these circumstances, investing in localisation capabilities powered by AI isn’t just about inclusivity. It becomes a strategic differentiator that drives customer satisfaction, loyalty and long-term brand equity.

These models aim to simplify the complexity, with a hybrid approach typically the most effective to unpack the linguistic nuances and understand the jargon.

A hybrid approach uses two different engines – one for general conversations and the other to capture context-specific entities, such as words, phrases, dates or numbers.

The trained algorithm then decides which transcription is the best or most relevant based on the data the contact centre is trying to capture and meet its specific objective, whether that is customer authentication or processing an insurance claim, as examples.

For the most effective solution, Connect has developed a hybrid automatic speech recognition (ASR) architecture designed specifically for real-world variability. It combines two complementary models – a contextual engine and an entity engine.

The contextual engine is a neural architecture optimised for conversational flow, able to recognise dialogue patterns, discourse structures and turn-taking. The entity engine is a precision-tuned ASR model focused on the accurate capture of entities, such as numbers, times, acronyms and currencies.

Both models generate candidate transcriptions. A bespoke contextual selection algorithm then evaluates each transcription against the expected conversational context and selects the one that makes the most semantic sense, not just the one with the highest raw confidence score.

This approach mirrors how humans listen: we interpret based on what fits the situation, not just what we hear phonetically.

However, speech recognition is only half the equation. Once the transcript is produced, language models take over, extracting meaning, intent and emotional tone.

In this regard, large language models (LLMs) are powerful but inefficient for localisation. They require staggering amounts of data to approximate understanding, yet still miss the nuances that define local speech.

Connect's approach inverts the usual paradigm. It maximises the intelligence of small, carefully curated datasets. It trains models to extract the full statistical and semantic value from a fraction of the data typically required.

This enables fast, real-time adaptation to niche domains – regional accents, industry-specific jargon or even the acoustic quirks of a call centre line in Nairobi versus Berlin.

In essence, Connect's models learn more from less, without sacrificing performance. Connect's proprietary models integrate entity and intent extraction with inverse text normalisation (ITN), transforming the fluidity of speech into structured, machine-usable information.

For example, a customer saying “a quarter past six tomorrow” becomes 18:15 2025-11-05, or “thirty bucks” becomes $30.

The system doesn’t just parse; it interprets, bridging the gap between natural conversation and machine precision, delivering capabilities that turn language localisation from translation to real understanding.