Introduction
Voice control is no longer confined to smart speakers and cloud-connected assistants. With advances in microcontrollers and low-power AI, it’s now possible to add voice recognition to even the simplest embedded projects — including something as playful as a toy car. However, there are multiple ways to achieve this. Two common approaches are TinyML-based onboard voice recognition and dedicated voice recognition modules.
Both methods let your device respond to spoken commands like “go,” “stop,” or “reverse,” but they differ in flexibility, complexity, and hardware requirements. Choosing between them depends on whether your priority is quick integration or full control over the recognition process.
How Each Approach Works
TinyML voice recognition involves training a custom machine learning model to recognize specific keywords. The model is deployed directly to the microcontroller, which also handles microphone input, feature extraction (such as MFCCs), and real-time classification. This gives complete freedom to define the vocabulary, language, and noise handling strategies.
A dedicated voice recognition module, on the other hand, comes with all of that functionality pre-built into the hardware. It has its own microphone, signal processing, and command detection firmware. Your main microcontroller simply receives the recognized command through a serial or I²C interface and acts on it. This offloads all the heavy processing and makes voice control more plug-and-play.
Hardware Requirements
A TinyML implementation usually needs a more capable microcontroller — something like an ESP32 or STM32 with sufficient RAM and CPU speed to handle real-time audio processing. You’ll also need an external microphone, preferably a digital I²S MEMS mic for low noise and compact size. All recognition runs on the main MCU, which means you have to consider both compute power and memory footprint when selecting components.
A dedicated module reduces the demands on your main MCU. Even a basic 8-bit Arduino Uno can handle the serial communication with the module. The microphone is built into the module, and the processing is done entirely on-board. This makes it ideal for projects where the main microcontroller is resource-limited or already busy with other tasks.
Flexibility and Accuracy
TinyML offers complete customization. You can train your model on any words in any language, adapt it to your accent, and fine-tune it for noisy environments. This is especially useful if your application involves uncommon vocabulary or if you want to include a wake word before executing commands. Accuracy can be improved over time by retraining with more targeted datasets.
A dedicated module is less flexible. Most support a fixed set of commands or allow you to program a small number of new words. Language support is typically limited to what the manufacturer provides. While these modules can perform well in controlled conditions, they may be harder to adapt for unique words, heavy accents, or background noise.
Development Effort
Building a TinyML voice recognition system requires more upfront work. You’ll need to collect or source audio data, preprocess it, train a model in TensorFlow Lite for Microcontrollers, quantize it for deployment, and integrate it into your firmware. Debugging involves checking not just your code but also the model’s performance.
A dedicated module greatly reduces development time. Wiring it up and reading command IDs through a serial port can be done in a single afternoon. There’s no need for training, dataset management, or model optimization. For many hobbyists or time-sensitive projects, this can be a major advantage.
Performance and Power Consumption
TinyML models running locally can have response times in the range of a few hundred milliseconds, depending on processing power and model complexity. Because the microcontroller is constantly sampling and processing audio, power consumption is higher, which can be an important factor in battery-powered designs.
Dedicated modules often have slightly faster recognition times since they are optimized purely for voice detection. They can also allow the main MCU to remain in a low-power state until a command is received, which can significantly extend battery life.
Cost Considerations
An ESP32 plus a MEMS microphone can be assembled for a relatively low cost, especially if you already have development boards on hand. The expense is mainly in your time investment for training and tuning. Dedicated modules are typically more expensive in terms of hardware but save weeks of development work.
Which One Should You Choose?
If you value full control, flexibility, and the ability to evolve your voice commands over time, TinyML is the way to go. It will require more effort, but it also opens the door to advanced features and a deeper understanding of AI on microcontrollers.
If your primary goal is rapid deployment and you are happy with a fixed set of commands, a dedicated voice recognition module is simpler and faster to integrate. It’s especially well-suited for first prototypes or when voice control is just one of many features in your project.
Conclusion
Both TinyML and dedicated voice recognition modules have their place in embedded development. TinyML shines when customization and adaptability are essential, while dedicated modules excel in simplicity and speed of integration. For a voice-controlled toy car, either approach can work — the choice depends on whether you want to invest your time in learning and building the recognition system from the ground up or prefer to focus on other aspects of your design.
Voice control on microcontrollers is now more accessible than ever, and understanding these two approaches gives you the flexibility to pick the best solution for your next project.