Collaborating Authors

speech recognition

The Impact of AI on Accessibility


Gerry Bayne: Welcome to EDUCAUSE Exchange, where we focus on a single question from the higher ed IT community and hear advice, anecdotes, best practices, and more. Students with disabilities are a vulnerable population in higher education. Yet the real percentage is likely higher, given that many choose not to disclose their disability to their institutions. Students with disabilities experience barriers to education that many other students do not. And they can have both visible and invisible needs. Their dropout rates are substantially higher and their graduation rates are significantly lower than those of non-disabled students. launches its new speech recognition technology for Indian defense – TechGraph


"These end-to-end voice translation system uses Automatic Speech Recognition (ASR), Machine Translation and Speech-to-Text to convert Mandarin to English and is designed to help armed forces, intelligence agencies and local law enforcement authorities in improving communication systems and giving substantial leeway to the Indian defense forces," the company in its statement said. The solution has a wide range of applications that includes cross border intelligence, voice surveillance, monitoring telephone/internet conversations, intercepting Radio/Satellite communication, and to bridge interactions during border meetings & joint exercises. Its unique features include noise reduction, dialect/accent detection, and support for all audio file formats. Speaking on the launch, Ananth Nagaraj, Co-founder & CTO, said, "AI-based Speech Recognition technology is a necessity and is quickly making its way in becoming part of modern warfare. We believe AI has the potential to transform and improve the communication systems and will help strengthen Indian Armed forces." "Understanding linguistic nuances such as phoneme and dialects is a challenge especially when it comes to Mandarin.

What is Natural Language Processing?


The individual sentences are mostly grammatically accurate and can be comprehended by us. The connection between sentences however is where it needs to improve. The writing does not always flow naturally from one paragraph to the next. The AI seems to only try to define what NLP is. It did try to elaborate on some occasions and move on to different areas of an NLP blog but was not very clear about what it meant. As it stands, this particular model needs to improve on understanding the context of writing a blog post on a higher level such as getting into some more detail of the applications of NLP, or how it works under the hood, or perhaps even featuring a blog generated by AI. Current innovations include using computer vision in conjunction with NLP to simulate a more complete form of perception. For instance, if we have an image of cats in a cup, that is recognized by computer vision, and we ask for images with cats minus the cup, the AI can return images of just cats. Or if we wanted images of cats minus the cup, plus boxes, the AI can return images of cats inside boxes instead of cups, all without having these pictures manually tagged.

Introduction 5 Different Types of Text Annotation in NLP


Natural language processing (NLP) is one of the biggest fields of AI development. Numerous NLP solutions like chatbots, automatic speech recognition, and sentiment analysis programs can improve efficiency and productivity in various businesses around the world. 

6 Major Branches of Artificial Intelligence (AI)


"Artificial Intelligence (AI) is the part of computer science concerned with designing intelligent computer systems, that is, systems that exhibit characteristics we associate with intelligence in human behavior – understanding language, learning, reasoning, solving problems, and so on." Artificial intelligence is the practice of computer recognition, reasoning, and action. It is all about bestowing machines the power of simulating human behavior, notably cognitive capacity. However, Artificial intelligence, Machine learning, and Data Science are all related to each other. In the commencement of this blog, we will gain expertise in Artificial Intelligence and its major six branches.

Artificial Intelligence Breakthrough is Expected to Fuel More Energy Efficient Devices


Recent advances in Artificial Intelligence (AI) will create more energy efficient devices that incorporate speech recognition, gesture recognition as well as electrocardiogram (ECG) classifications. As a result, more elaborate AI will be embedded in chips for local devices such as smartphones and smartwatches as opposed to the cloud. Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands, working with IMEC/Holst Research Centre from Eindhoven, announced they have achieved a mathematical breakthrough that would leverage AI technology to increase energy efficiency on local devices. This will make the applications running on these devices more robust and even more secure. The results have been published in a research paper (by Bojian Yin, Federico Corradi, and Sander M. Bohté) of the International Conference on Neuromorphic Systems.

Google now has iOS widgets for Gmail, Drive and Fit, with more on the way


When iOS 14 came out in September, Google was one of the first third-party developers to come out with a home screen widget for one of its iPhone and iPad apps. Some of the new widgets look more useful than others. For instance, the one that comes with Google Fit looks great. Not only does it let you see the progress you're making toward your daily step and heart points goals, but it also provides you with a weekly breakdown of your activity. However, the Gmail one looks like it could use some work. As you can see from the screenshot Google shared, you can use its new widget to search your inbox and start composing an email.

Federated Learning for Privacy-Preserving AI

Communications of the ACM

There has been remarkable success of machine learning (ML) technologies in empowering practical artificial intelligence (AI) applications, such as automatic speech recognition and computer vision. However, we are facing two major challenges in adopting AI today. One is that data in most industries exist in the form of isolated islands. The other is the ever-increasing demand for privacy-preserving AI. Conventional AI approaches based on centralized data collection cannot meet these challenges.

Dimitris Vassos, CEO, Co-founder, and Chief Architect of Omilia – Interview Series


Dimitris Vassos is the CEO, Co-founder, and Chief Architect of Omilia, a global conversational intelligence company that provides advanced automatic speech recognition solutions to companies and organizations in North America, Canada, and Europe. Dimitris has significant experience in the field of applied speech and artificial technology, specifically, natural language understanding (NLU), speech recognition, and voice biometrics. What initially attracted you to AI? Human-Machine interfaces have mesmerized me since I was a child. In 1984, I had one of the first home computers. I remember I had programmed it to control our home lighting using sound recognition.

How enterprises will benefit from AI and voice data in the post pandemic world


The promise of artificial intelligence finally came good in 2018 and 2019, with a wider adoption of AI - from its use in detecting and combating fraud in financial institutions, through to sophisticated analytics tools in contact center. There are a host of use cases showing the value of a future-facing AI strategy, leveraging accurate and collectable data to save time, improve efficiencies, and reduce operational costs. In fact, a recent KPMG report states that five of the most AI-mature companies are spending $75m annually on AI talent, indicating the increasing importance of using AI by business leaders. The same report also finds that analysis of voice data is a high priority AI initiative, but there are some critical foundational elements that are maybe not being given the consideration they should. Organizations interested in adopting this new technology - and those that already are - must remember that AI and analytics tools are fueled by data, and the output is directly correlated to the quality of the input.