The human voice is the most valuable biosignal for communication, humans can speak 150 words per minute on average, but can be up to 40 words. The human voice is essential not only in telecommunication, but also in human-machine interaction (HMI) and the Internet of things (IoT), such as remote-control systems, home automation, and smart industrial infrastructure. Everything from wearable fitness trackers and wireless earbuds to microwaves, refrigerators, and robot vacuum cleaners has been equipped with voice recognition capabilities. As a testament to this adoption of voice-controlled devices the entire speech and voice recognition industry is expected to grow to $21.5B by 2024 at a CAGR of 19.18%. 
Voice Recognition Hardware
The basic hardware design of voice recognition involves three main elements. They’re a microphone circuit, a microcontroller circuit, and an LCD display. The microphone circuit is connected to the analog to digital converter (ADC) of the controller that converts the incoming analog speech into digital signals. The microcontroller memory is pre-stored with a set of words and phrases. Once a word has spoken the microphone (Mic) receives it and further processes it, removing background noise and normalizing the amplitude variations. Further, the word passes through a digital filter in the microcontroller where it’s re-sampled to accommodate the rate at which the speaker delivered and displays the words spoken in an LCD. The use of microcontrollers lowers the BOM cost by up to 50% when compared with high-end microprocessor implementations. 
Embedded Offline Speech
The Increased development of AI technologies and privacy concerns raised by cloud-connected voice devices and the constant need for network connection brings in the need for more offline devices. The best example is the wake engines on our mobile phones. It’s a piece of code and a trained network that monitors a special keyword that activates the voice assistant. These wake word technologies are designed to operate with low latency by dedicating a small portion of edge computing resources to process microphone signals, while the other parts of the system remain idle saving your battery consumption.
Edge Computing Devices
Edge computing devices help separate the user’s voice from other surrounding sounds. If the user moves around, voice tracing algorithms running on the device adjusts the signals from the microphones and focus on the source of the voice. This is happening with the support of superior edge processing power by adopting heterogeneous computing architectures integrating engines such as CPUs, GPUs, and DSPs into a single system-on-chip which assigns the workloads to the efficient engine thus improving performance, power efficiency, and cost-effectiveness.
Voice command technology is rapidly growing in everything from smart speakers to toys. The global voice recognition market is expected to reach 127.58B by 2024. The development and evolution of voice recognition AI have led to the proliferation of devices in everyday life that can be controlled by voice. The technology is unlocking new ways to serve the desire for personalization, where AI-backed voice assistants become able to understand the users in deeper and complex ways. This paradigm will change the relationship between users and the voice assistants becoming virtual companions, counselors, and even friends.
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