How does speech recognition work?

Everything starts with sound. A customer calls and says: “I want to know where my order is.” That isn’t understood in one go. The system works through roughly four steps:

1. Audio → text The system converts the sound into plain text. The audio is broken down into small fragments and analysed for phonemes. Based on this, the system recognises words and constructs a sentence.

2. Text → interpretation That sentence is then analysed to determine what the customer actually wants. A language model looks at the context and identifies the intent behind the words: Customer wants to track their order (track & trace request).

3. Interpretation → action The recognised intent is linked to an action in the system: → Retrieve track & trace information → Or route to the correct department

4. Text → audio (the response) Finally, the system formulates a reply. The outcome (“Your package will be delivered tomorrow”) is first composed as text and then converted to speech.

In less than a second, the system translates sound into understanding, understanding into action, and back again into a naturally-sounding response. For the customer, it feels like one seamless conversation. While multiple intelligent steps are happening behind the scenes.

Up to here, it still sounds fairly manageable. But the real complexity lies in the connection with underlying systems. Because “track my order” isn’t an answer, it’s a trigger. The speech system then needs to do several things, almost simultaneously:

Identify the right customer The system first needs to know who is calling. In many cases, this happens automatically via caller ID: the phone number is matched to a customer profile in the CRM. If that isn’t sufficient, the system can ask the caller for additional information. Such as a customer number or postcode and house number. Based on that data, the correct customer is located in the underlying systems.

Retrieve the right data Once the customer is identified, the system needs to pull the relevant information: from a CRM, order management system or e-commerce platform where orders are stored.

Select the right information A customer often has multiple orders. The system needs to determine: which one does this refer to? Is it the most recent order, or a specific one the customer mentioned? That requires logic, not just data retrieval.

Translate into an understandable answer Raw data (“status: shipped, ETA: 16:00”) needs to be turned into something human: “Your package will be delivered tomorrow between 2:00 and 4:00 PM.” And all of this happens within a matter of seconds.

Where things often go wrong In practice, the problem is rarely the speech itself. Things break down in the layer behind it: systems that aren’t connected, missing data, or logic that hasn’t been set up properly.

Another common gap: clear fallbacks. What happens when the required information simply isn’t there? If no delivery date is known yet, the system also needs to know what to say. For example: “Your order is on its way, but we don’t have an exact delivery time yet. You’ll receive an update as soon as it’s available.”

Without these safety nets, a conversation quickly stalls or produces a vague or incorrect answer. The voicebot technically knows what the customer wants, but simply can’t respond well.

A great example of where theory and practice collide: postcodes. Take: 1234 AB

People say it in all sorts of ways: “twelve thirty-four AB”, “one two three four AB”, “12 34 Alpha Bravo”, or “one two three four Anton Bernhard.”

For people, that’s perfectly logical. For a system, it isn’t. More happens here than just recognising speech. The system needs to combine several things: speech recognition (what is being said?), context (this field expects a postcode) and validation (4 digits + 2 letters).

Based on that, the system can map different variants back to the same result. So: “12 34 Anton Bernhard” is interpreted as: 1234 AB.

This is precisely the difference between simple transcription and genuinely understanding. Not just hearing what someone says, but grasping what they mean, even when it’s expressed in multiple ways.

This is just a small piece of the whole picture. We haven’t even touched on accents and dialects, background noise, or people changing their mind mid-sentence. All factors that influence how well a voicebot performs. Which is also why it tends to be far more complex in practice than it looks on paper.

That’s also what makes the difference between a voicebot that “can do something” and a voicebot that genuinely works for your customers.

Want to know what this looks like in your situation or where things might be going wrong for you? Feel free to get in touch. We’re happy to take a look with you. We help organisations make voicebots not just smart, but truly useful in practice.