Voice assistants, as we call them, actually work by combining many components, from a simple microphone to a complex artificial intelligence system. Fundamentally, they start by converting sound signals into digital data. The microphone converts sound waves in the environment into electrical signals. Then, these signals are processed by speech recognition engines—meaning they are broken down into words and sentences.
Up to this point, the process is similar in all systems. The real difference lies in what happens in the background. After the spoken words are classified according to the vocabulary, they are sent to remote servers, what we call the cloud. Here, natural language processing (NLP) algorithms come into play. Machine learning models are used to extract the meaning of the sentence, determine the intent, and produce the most accurate response. So, in reality, the "brain" behind the scenes is located there.
Once the response is generated, the text-to-speech (TTS) system kicks in. This system converts the response into a sound wave and sends it back to the user through the speaker. All this process happens within milliseconds—backed by continuously learning and evolving models in the background.
Another important point is privacy. Whether conversations are recorded, and how long they are stored, varies according to national and international regulations. To allow users to make their own choices on this matter, systems typically offer options to delete voice recordings or have them automatically erased. Essentially, voice assistants work by combining a series of complex processes, enabling not just voice commands but also data exchange through speech.
How do voice assistants work? Basic concepts
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If you want to explore the part that converts audio signals into digital data, you can check out some publicly available APIs. For example, Mozilla’s "DeepSpeech" is open-source and supports Turkish; after sending an audio recording, you can receive the sentences as text. In a project I was working on, I used DeepSpeech’s Turkish model to capture voice commands in JSON format and trigger buttons in the interface.
If you want to dive into the hardware side, you can try a cheap MEMS microphone (connecting it to an ESP32 or Raspberry Pi Zero). In my early experiments, I started with the MAX9814 sound module, sending the raw data from the device to an ADC and then to the speech recognition engine. In fact, with an Arduino Nano connected to a PC, I bypassed the speech recognition system entirely and coded a simple trigger based on a threshold like "signal strength >500." Above all, pay attention to voltage regulation—the microphone supply isn’t stable at 3.3V, or the signal gets heavily distorted.
Voice assistants function similarly to the automated voice response systems in classic telephone exchanges, but the former are far more advanced. In telephone exchanges, microphones capture sound and convert it into digital signals, recognizing basic commands (e.g., "Press zero"), but the voice recognition engine is highly limited—mostly matching pre-defined words. Modern voice assistants, on the other hand, leverage AI-powered natural language processing (NLP) to continuously improve, understand context, and even execute complex commands. So while both systems capture speech, assistants have reached near-"understanding spoken language" levels thanks to deep learning.