Have you ever wondered how Apple's personal assistant 'Siri works? How do voice assistants recognize us? Learn the tech behind Apple Siri to know how its voice recognition technology work.
Have you ever wondered how Apple’s personal assistant “Siri” works? What happens when you ask “Hey Siri, what’s My Schedule”. What tech did apple use to answer your questions?
How do these voice assistants recognize us? From voice biometrics in security applications to helping blind people, voice recognition has many applications. Companies like Apple, Google, Amazon, Microsoft, etc are all using voice recognition in their devices and actively doing research. As compared to speech recognition, the voice recognition market is expected to grow at a higher rate, owing to the growing use of voice recognition in multi-factor authentication systems in BFSI, government, automobile, and defense verticals.
Let’s understand the science behind Apple’s Siri. Siri is an AI-powered personal assistant which has gained immense popularity in today’s lifestyle. This personal assistant offers crazy features that possess the ability to transform our lives better. Siri is a listening device embedded in Apple’s iOS and macOS. Nowadays, various assistants are available in the market like Amazon Alexa, OK Google, Cortana, Bixby, and many more. But what makes Siri unique from all other personal voice assistants.
In this session, we are going to talk about in this session on how Siri’s voice recognition works?
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Siri is an in-built voice-controlled personal assistant available for Apple users. Siri is designed to offer users a seamless way of interacting with your iPhone, iPad, iPod Touch, Apple Watch, HomePod, or Mac. The user needs to speak to her and she speaks back to them to find or do what they need. Users can ask her questions, tell her to show them something, or issue her with commands for her to execute on their behalf, hands-free.
Siri has access to every other built-in application on your Apple device - Mail, Contacts, Messages, Maps, Safari, and so on - and can perform commands on those apps to present data or search through their databases whenever she needs to. Ultimately, Siri is a personal assistant for Apple users.
Siri works mainly on the principle of two primary technologies, natural language processing, and speech recognition.
Natural Language Processing: NLP is a computer science branch that deals with artificial intelligence programs whose function is to understand and interpret human language. It reduces the word error rate of speech recognition engines. It also produces phenomenal results in speech recognition.
Speech Recognition: Speech Recognition is a process of converting a human speech into a textual form. Let’s say when you trigger Siri by saying “Hey Siri”, a powerful speech recognition system by Apple kicks off in the back-end and converts our audio into its textual form – “Hey Siri.” This is an extremely challenging task because humans have a highly diverse set of tones as well as accents. Some people speak slowly, some speak fast. Characteristics of male and female voices are also very different.
The engineers at Apple trains machine learning models on large datasets in order to create efficient speech recognition models for Siri. These models are trained with highly diverse datasets that comprise the voice samples of a large group of people. This way, Siri is able to speak in various accents.
Feature extraction is a dimensionality reduction process, where an initial set of the raw data is divided and reduced to more manageable groups. The most important characteristic of these large data sets is that they have a large number of variables. These variables require a lot of computing resources to process them. So feature extraction in machine learning helps to get the best feature from those big data sets by selecting and combining variables into features, thus, effectively reducing the amount of data.
Feature extraction is a process to reduce the number of features in a dataset by creating new features. Another most commonly used technique to reduce the number of features is feature selection. The difference between feature extraction python and feature selection is that feature selection aims to rank the existing features in the dataset and discard less important ones. Feature extraction uses the following algorithms to reduce the number of features.
Neural networks, also known as artificial neural networks (ANNs) are a subset of machine learning and are at the heart of deep learning algorithms. Deep neural networks interpret sensory data through a kind of machine perception, labeling, or clustering raw input. They recognize patterns that are numerical and contained in vectors, into real-world data(images, sound, text, or time series) that must be translated.
The structure of neural network machine learning is inspired by the human brain in a similar way that biological neurons signal to one another. An artificial neural network consists of node layers, containing an input layer, one or more hidden layers, and an output layer. Each node connects to one another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sends the data to the next layer of the network. No data is passed along to the next layer otherwise.
In this session, you can get your next convolutional neural network project.
There are many types of neural networks based on their: Structure, Data flow, Neurons used and their density, Layers and their depth activation filters, etc.
Below are the top Neural Network examples in the real world.
Digital image processing is a process of manipulation of digital images through a digital system. The input of that system is a digital image and the system process that image using efficient algorithms, and gives an image as an output. The most common example is Adobe Photoshop. Digital image processing can be your next Image processing project.
Digital image processing is one type of method used for image processing using python. Another method for image processing is Analogue.
Major image processing techniques are as follows-
Some of the major fields in which digital image processing is widely used are mentioned below -
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