Speech Recognition, also known as Speech Recognition, is a computer software program or hardware device capable of decoding the human voice. Speech recognition is often used to interact with devices, execute commands, and write without using a keyboard, mouse, or pressing keys. Today this is done on computers with ASR software programs (Automatic Speech Recognition). Many ASR programs require the ASR program to be "trained" to recognize speech so that it can convert speech to text more accurately. For example, when you say, "Open the Internet," your computer opens your Internet browser.
The first ASR device was used in 1952 and recognized single digits spoken by the user (it was not computer controlled). Today, ASR programs are used in many industries, including medical, military (such as the F-16 fighter), telecommunications, and personal computing (such as hands-free computing).
Examples of where you might have used Voice Recognition:
As speech recognition improves, there are more and more places implementing speech recognition, and it's likely that you've used it before. Below are some examples of where speech recognition can occur.
- Automated Phone Systems
- Google Voice
- Digital Assistant
- Car media Stereo
Types of Voice Recognition Systems
Automatic speech recognition is an example of speech recognition. Below are other examples of speech recognition systems.
- Speaker Dependent System
- Speaker Independent System
- Discrete Speech Recognition
- Continuous Speech Recognition
- Natural language Processing
VOICE RECOGNITION MODULE:
The Voice Recognition Module is a compact and easy-to-use voice recognition card. This product is a speaker-dependent speech recognition engine. Supports up to 80 voice commands in total. You can use up to 7 voice commands simultaneously. Any sound can be trained as a command. user needs
Before recognizing voice commands, we first train the module.
On this board it has two control options.
Serial port (full functionality), general purpose input pin (partial functionality). Universal Output pins on the board can generate different types of waves while the corresponding voice command is executed. Any sound can be trained as a command. Users must first train the module before they can recognize voice commands.
PARAMETER
- Voltage: 4.5-5.5V
- Current: <40mA
- Digital Interface: 5V TTL Level for UART interface and GPIO
- Analog Interface: 3.5mm Mono-Channel microphone Connector + microphone pin interface
- Size: 31mm X 50mm
- Recognition Accuracy 99% (under ideal conditions)
FEATURES:
- Support Maximum 80 Voice Commands, with each voice 1500ms (one or two words speaking)
- Maximum 7 voice commands effective at the same time
- Arduino Compatible
- Easy Control: UART/GPIO
- User-control General pin output
- CONNECTIONS TO ARDUINO
- Arduino -> VR Module
- 5V -> 5V
- 02 -> TX
- 03 -> RX
- GND -> GND
PROJECTS WITH VOICE RECOGNITION MODULE
- Voice Controlled Robot
- Speech Noise Detection
- Smart Home with Voice Control
- Long distance speech recognition
- Smart Assistant
WORKING
- First, we need to train the module with voice instructions for each group. The group must then be imported before the five voice commands can be recognized within this group. If you need to implement statements in other groups, you must import the group’s first. Only one group can be active at a time
- There are two ways to use this module, using the serial port or the built-in GPIO pins. The V3 board can store up to 80 voice commands, each lasting 1500ms. It doesn't convert commands to text but compares them to a series of pre-recorded voices. Technically, there is no language barrier in using this product. You can record commands in any language, or literally any sound to use as a command. Therefore, it must be trained first before it can recognize voice commands.
- When using the module with GPIO pins, the module provides outputs for only 7 of the 80 instructions. This method requires selecting seven commands to load into the recognizer. When any of these voice commands are recognized, the recognizer will send output to the appropriate her GPIO pin. This is what he uses on his Arduino, so he doesn't have to worry about limited functionality.
- Microphone selection and noise in the environment play an important role in affecting module performance. We recommend choosing a sensitive microphone to reduce background noise while executing commands that maximize the module's performance.
- Connect the Circuit to the computer.
- Launch the Arduino IDE.
- Check whether you've selected the right Arduino board. (Tools -> Board)
- Check if the right COM port is selected. (Tools -> Port)
- Now open the sample program for training the module.
- Go to File -> Examples -> VoiceRecognitionV3 -> vr_sample_train
- Upload the code to Arduino and wait until the code gets uploaded. (Ctrl + U)
- Open the Serial Monitor. (Ctrl + Shift +M)
- Make sure that the baud rate is set to 115200 and the "Newline" option is selected.
- If everything is fine, a menu will be shown on the serial monitor.
- There are several commands that you can type into the serial monitor to program the module, here we'll be using the "train" command to train the module.
- The V3 has a capacity to store 80 voice commands, each with a duration of 1500 Ms. Each command is stored in an address starting from 0 to 79.
- By using the "train" command, we're storing a voice command into a specific address, so you should specify the address in the command.
- The syntax of the command goes like this: train address for example: train 0, train 20, train 79.
- We will require two voice commands for controlling the LED. One command to turn it ON and the other one to turn it OFF.
- Enter the command in the serial monitor followed by the address you want to store it. e.g.: train 20.
- After you've entered the command, wait for a message to appear on the serial monitor that says, "speak now". Now speak your command for turning ON the LED into the microphone clear and loud enough.
- If the command is clear enough, another message will show up asking you to speak again. Speak it again to register the command.
- The code will ask you to repeat the command if some noise occurs during the recording or if the sound is not clear enough. The quality of your microphone has a considerable role over here. You may fail to register a command if your microphone isn't good enough. Also train the board in a noise free environment.
- Once you've successfully entered a voice into the module, repeat the same process to input the voice command for turning OFF the LED. Remember to store the command in a different address. For e.g.: train 30.
- If you've successfully loaded both commands, you're now ready to upload the code for controlling the LED.
Conclusion
Voice recognition technology has become increasingly common in our daily lives. From virtual assistants like Siri and Alexa to security systems and medical devices, voice recognition has endless applications. There are various types of voice recognition systems, including speaker-dependent and speaker-independent systems. Voice recognition modules are readily available and can be integrated into various projects, making them smarter and more intuitive. However, while voice recognition technology has come a long way, it still has some limitations that need to be addressed. Overall, voice recognition is an exciting technology with limitless potential.
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