Schlagwort: speech recognition

  • Fortune Cat uses embedded speech recognition to predict your future

    Fortune Cat uses embedded speech recognition to predict your future

    Reading Time: 2 minutes

    For well over one hundred years, people have been constructing machines that dispense fortunes to those who ask at the insertion of a coin and the push of a button. In modern days, this has taken the form of mobile apps that can be far more expansive, albeit with a lack of physical interaction. Seeing an opportunity to use an embedded speech recognition model in this kind of application, the Electronic Cats team built the aptly named Fortune Cat just in time for some Halloween fun.

    This small device, based on the Arduino Nano 33 BLE Sense, takes advantage of the onboard microphone to listen for words being spoken at the small cube. Performing the language processing is Cyberon’s Arduino Speech Recognition Engine, which was configured to listen for the wake phrase “Fortune Cat” and then later respond to “tell me my future” as its command phrase. After generating the model online, it was incorporated into the code via the DSpotter software development kit that lets the program check if either the wake or action phrase has been said and then act accordingly.

    In this case, asking Fortune Cat for your future will present one of 20 random phrases that get displayed on the OLED mounted to the top of the 3D-printed enclosure. To read more about how the Electronic Cats crew created and configured Fortune Cat, you can check out their write-up here on Hackster.io or watch their video below!

    [youtube https://www.youtube.com/watch?v=KrPEl2D1z5E?feature=oembed&w=500&h=281]

    The post Fortune Cat uses embedded speech recognition to predict your future appeared first on Arduino Blog.

    Website: LINK

  • Instantly understand 40+ languages, with Speech Recognition Engine

    Instantly understand 40+ languages, with Speech Recognition Engine

    Reading Time: 2 minutes

    Voice commands are a contactless, hands-free, natural way to interact with devices, equipment, and machines of all kinds: no wonder they are increasingly popular! With speech recognition technology advancing faster than you can say “users expect it,” developers often need to find ways to integrate it in their new projects. This, however, requires them to take the time to train a smart device to listen for wake-up words, understand commands… and potentially start over for every new voice or language.

    To cut through all the hassle and help you integrate speech recognition in the easiest and fastest way, Arduino Pro has released Speech Recognition Engine: a powerful, ready-to-use and extensive software library born out of the collaboration with worldwide leader in the field, Cyberon.

    Let’s break it down:

    • Widely compatible: Speech Recognition Engine is compatible with various Arduino Nano and Portenta boards as well as with the Arduino IDE, and requires no additional hardware, software or internet connectivity. Integrate it in any new or existing project you have in mind.
    • Zero training time: Because it uses text input to define voice commands, Speech Engine Recognition translates what you say into instant results, with flexibility over the speaker’s actual voice, tone or accent.
    • It speaks your language: Its AI/ML engine understands commands in 40+ languages. This means you can quickly configure multiple wake-up words and sequences, in any major language spoken in the world.
    • Great for multiple users: With Speech Recognition Engine, your next smart device will be able to listen to different people without skipping a beat, making it perfect for applications ranging from information kiosks, vending machines and lockers, smart beds in hospitals, emergency call systems, and more. Its ability to filter background noises makes it especially suitable for public spaces and busy factory floors.
    • Industrial-grade performance: As part of Arduino Pro’s growing ecosystem of advanced professional solutions, Speech Recognition Engine was developed for industrial and building automation applications such as hands-free control of machinery, equipment, and devices of all kinds.

    You can find out more details from the dedicated page. Or, if you’ve had enough of the talk and want to quickly and effortlessly integrate speech recognition in your next project, head to the Arduino Store and get started with the Speech Engine Recognition library!

    The post Instantly understand 40+ languages, with Speech Recognition Engine appeared first on Arduino Blog.

    Website: LINK

  • This Arduino device can detect which language is being spoken using tinyML

    This Arduino device can detect which language is being spoken using tinyML

    Reading Time: 2 minutes

    Arduino Team — December 8th, 2021

    Although smartphone users have had the ability to quickly translate spoken words into nearly any modern language for years now, this feat has been quite tough to accomplish on small, memory-constrained microcontrollers. In response to this challenge, Hackster.io user Enzo decided to create a proof-of-concept project that demonstrated how an embedded device can determine the language currently being spoken without the need for an Internet connection. 

    This so-called “language detector” is based on an Arduino Nano 33 BLE Sense, which is connected to a common PCA9685 motor driver that is, in turn, attached to a set of three micro servo motors — all powered by a single 9V battery. Enzo created a dataset by recording three words: “oui” (French), “si” (Italian), and “yes” (English) for around 10 minutes each for a total of 30 minutes of sound files. He also added three minutes of random background noise to help distinguish between the target keywords and non-important words. 

    Once a model had been trained using Edge Impulse, Enzo exported it back onto his Nano 33 BLE Sense and wrote a small bit of code that reads audio from the microphone, classifies it, and determines which word is being spoken. Based on the result, the corresponding nation’s flag is raised to indicate the language.

    You can see the project in action below and read more about it here on Hackster.io.

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    Website: LINK

  • Nandu’s lockdown Raspberry Pi robot project

    Nandu’s lockdown Raspberry Pi robot project

    Reading Time: 2 minutes

    Nandu Vadakkath was inspired by a line-following robot built (literally) entirely from salvage materials that could wait patiently and purchase beer for its maker in Tamil Nadu, India. So he set about making his own, but with the goal of making it capable of slightly more sophisticated tasks.

    [youtube https://www.youtube.com/watch?v=Y5zBCSHnulc?feature=oembed&w=500&h=281]

    “Robot, can you play a song?”

    Hardware

    [youtube https://www.youtube.com/watch?v=7HJzhZYlHhU?feature=oembed&w=500&h=281]

    Robot comes when called, and recognises you as its special human

    Software

    Nandu had ambitious plans for his robot: navigation, speech and listening, recognition, and much more were on the list of things he wanted it to do. And in order to make it do everything he wanted, he incorporated a lot of software, including:

    [youtube https://www.youtube.com/watch?v=KTHh8QU70nc?feature=oembed&w=500&h=281]

    Robot shares Nandu’s astrological chart
    • Python 3
    • virtualenv, a tool for creating isolating virtual Python environments
    • the OpenCV open source computer vision library
    • the spaCy open source natural language processing library
    • the TensorFlow open source machine learning platform
    • Haar cascade algorithms for object detection
    • A ResNet neural network with the COCO dataset for object detection
    • DeepSpeech, an open source speech-to-text engine
    • eSpeak NG, an open source speech synthesiser
    • The MySQL database service

    So how did Nandu go about trying to make the robot do some of the things on his wishlist?

    Context and intents engine

    The engine uses spaCy to analyse sentences, classify all the elements it identifies, and store all this information in a MySQL database. When the robot encounters a sentence with a series of possible corresponding actions, it weighs them to see what the most likely context is, based on sentences it has previously encountered.

    Getting to know you

    The robot has been trained to follow Nandu around but it can get to know other people too. When it meets a new person, it takes a series of photos and processes them in the background, so it learns to remember them.

    Nandu's home made robot
    There she blows!

    Speech

    Nandu didn’t like the thought of a basic robotic voice, so he searched high and low until he came across the MBROLA UK English voice. Have a listen in the videos above!

    Object and people detection

    The robot has an excellent group photo function: it looks for a person, calculates the distance between the top of their head and the top of the frame, then tilts the camera until this distance is about 60 pixels. This is a lot more effort than some human photographers put into getting all of everyone’s heads into the frame.

    Nandu has created a YouTube channel for his robot companion, so be sure to keep up with its progress!

    Website: LINK

  • Start a 1976 Jeep with voice commands using a MacBook and an Arduino

    Start a 1976 Jeep with voice commands using a MacBook and an Arduino

    Reading Time: < 1 minute

    Start a 1976 Jeep with voice commands using a MacBook and an Arduino

    Arduino Team — March 9th, 2020

    After being given a 2009 MacBook, John Forsyth decided to use it to start a 1976 Jeep via voice control.

    The build uses the laptop’s Enhanced Dictation functionality to convert text into speech, and when a Python program receives the proper keywords, it sends an “H” character over serial to an Arduino Uno to activate the vehicle.

    The Uno uses a transistor to control a 12V relay, which passes current to the Jeep’s starter solenoid. After a short delay, the MacBook then transmits an “L” command to have it release the relay, ready to do the job again when needed!

    As a fan of Iron Man, Forsyth channeled his inner Tony Stark and even programmed the system to respond to “JARVIS, let’s get things going!”

    [youtube https://www.youtube.com/watch?v=j-GmDpiXWng?feature=oembed&w=500&h=281]

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