What is Deep Learning?

Deep learning is a subset of machine learning that aims to train machines via algorithms (neural networks) inspired and designed after the structure of the biological brain. The primary focus of deep learning is to teach machines what comes naturally to humans – to learn through examples and experience.

Deep learning uses neural network architectures comprising multiple hidden layers within, ranging from 2-3 to 150 layers. This is where the name “deep” learning comes from. Usually, large sets of data (labeled) data are used to train deep learning models and neural network architectures. This allows the models to learn and adapt to the features directly from the dataset instead of relying on manual feature extraction.

Deep learning trains computer models to perform classification tasks directly from text, image, or audio data. When trained adequately, deep learning models attain high-level accuracy, sometimes even outperforming human capabilities. It is deep learning technology that powers autonomous cars, voice recognition systems, virtual assistants, fraud detection systems, and natural language processing

Why Does Deep Learning Matter?

Deep learning is rapidly gaining popularity across industries because it promises unmatched levels of accuracy and efficiency. Although deep learning models are generally trained on labeled data, once trained, they become adept at “unsupervised learning,” meaning they can extract valuable insights from raw (unstructured or unlabeled) data.

This is precisely why deep learning is now being used in many areas – from making speedy and accurate medical diagnoses to enhancing personalization for eCommerce companies.

Here are a few applications of deep learning

Automated driving – Companies like Google and Tesla are experimenting with deep learning to improve automated driving. Powered by deep learning, self-driving cars can automatically detect objects like stop signs, traffic lights, vehicles, pedestrians, etc.

Industrial automation – Today, an increasing number of companies use deep learning technology to improve worker safety in manufacturing units, particularly around heavy machinery. Deep learning systems can automatically detect when workers/objects are unsafe and alert them, preventing unwanted accidents.

Aerospace – Aerospace organizations use deep learning to identify objects from satellites to locate areas of interest and identify safe/unsafe zones for troops and successful landing of spacecraft.

Medical research – Deep learning has extensive use cases in the field of medical research. For instance, in collaboration with NantWorks, researchers at UCLA developed a microscope powered by AI and deep learning to detect cancer cells within a few milliseconds – hundreds of times faster than any other method.

Virtual assistance – Deep learning is the technology behind speech translation and automated hearing. Smart personal assistants like Alexa and Siri are two of the best examples of deep learning applications for virtual assistance.

Visual recognition – Deep learning technology is used to develop state-of-the-art image recognition systems. These systems can classify and sort images according to multiple factors like location, dates, faces, objects, and events.

Speech recognition refers to a computer interpreting the words spoken by a person and converting them to a format that is understandable by a machine. Depending on the end-goal, it is then converted to text or voice or another required format.

Speech recognition AI applications have seen significant growth in numbers in recent times as businesses are increasingly adopting digital assistants and automated support to streamline their services. Voice assistants, smart home devices, search engines, etc. are a few examples where speech recognition has seen prominence. As per Research and Markets, the global market for speech recognition is estimated to grow at a CAGR of 17.2% and reach $26.8 billion by 2025.

Speech Recognition and Artificial Intelligence

Speech recognition is fast overcoming the challenges of poor recording equipment and noise cancellation, variations in people’s voices, accents, dialects, semantics, contexts, etc using artificial intelligence and machine learning. This also includes challenges of understanding human disposition, and the varying human language elements like colloquialisms, acronyms, etc. The technology can provide a 95% accuracy now as compared to traditional models of speech recognition, which is at par with regular human communication.

Furthermore, it is now an acceptable format of communication given the large companies that endorse it and regularly employ speech recognition in their operations. It is estimated that a majority of search engines will adopt voice technology as an integral aspect of their search mechanism.

This has been made possible because of improved AI and machine learning (ML) algorithms which can process significantly large datasets and provide greater accuracy by self-learning and adapting to evolving changes. Machines are programmed to “listen” to accents, dialects, contexts, emotions and process sophisticated and arbitrary data that is readily accessible for mining and machine learning purposes.

Voice AI is a conversational AI tool that uses voice commands to receive and interpret directives. With this technology, devices can interact and respond to human questions in natural language.

With the ability to understand the human language and communicate with them, the voice AI chatbot has offered a great opportunity to businesses to serve customers. It helps speed up processes, increase productivity and scale operations

Global Impact of Speech Recognition in Artificial Intelligence

Speech recognition has by far been one of the most powerful products of technological advancement. As the likes of Siri, Alexa, Echo Dot, Google Assistant, and Google Dictate continue to make our daily lives easier, the demand for such automated technologies is only bound to increase.

Businesses worldwide are investing in automating their services to improve operational efficiency, increase productivity and accuracy, and make data-driven decisions by studying customer behaviour and purchasing habits.

AI has facilitated an exponential growth in a wide range of sectors of the global economy. It is estimated that AI’s contribution to the global economy will hit $15.7 trillion in 2030, which is significantly higher than China and India’s combined output.

How does speech recognition work?

Speech recognition is the process of converting spoken words into machine readable data. This can be done by either good old rule-based approaches or by applying machine learning techniques. Rule-based approaches have been used in computers for speech recognition since the 60s. They are initially trained by hand and require a lot of effort to maintain over time. Machine learning approaches, on the other hand, are trained automatically from a set of training data and require little maintenance over time. They are therefore more efficient in the end, although initial training is often quite expensive.

The voice AI is based on understanding the human language and interpreting the same to offer appropriate results. AI programming perfects its algorithms to constantly provide the best rational answer. A mixture of AI and automation helps develop speech systems.

Like when two people communicate, there is encoding and decoding of the message; voice AI works similarly. Below, we discuss the steps involved in speech recognition in AI.

Written by: Deepshikha Niyogi