"Automatic speech recognition (ASR) is one of the fastest growing and commercially most promising applications of natural language technology. Speech is the most natural communicative medium for humans in many situations, including applications such as giving dictation; querying database or information-retrieval systems; or generally giving commands to a computer or other device, especially in environments where keyboard input is awkward or impossible (for example, because one's hands are required for other tasks)."
– from Linguistic Knowledge and Empirical Methods in Speech Recognition. By Andreas Stolcke. (1997). AI Magazine 18 (4): 25-32.
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.
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 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.
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.
Artificial Intelligence (AI) technology evolves rapidly, and it has great potential in the future. According to the latest reports, the market size of AI is projected to reach $266.92 billion by 2027 with a Compound Annual Growth Rate (CAGR) of 33.2%. A lot of world-known brands and tech companies are already using AI-powered solutions to improve the service, engage customers, enhance customer experience, and increase efficiency and productivity. Text generation, face and speech recognition, automated translation, drug discovery are a few AI achievements that are worthy of your attention. AI-powered solutions are used by dozens of companies and implemented in different fields, changing a lot of industries and reshaping the landscape of health, learning, daily living, and so on.
Your Raspberry Pi computer is like an electronic brain – and with the Adafruit Voice Bonnet, you can give it a mouth and ears as well! Featuring two microphones and two 1 Watt speaker outputs using a high-quality I2S codec, this Pi add-on will work with any Raspberry Pi with a 2 20 connector – from the Pi Zero up to the Pi 4 and beyond (basically all but the very first ones made). The on-board WM8960 codec uses I2S digital audio for great quality recording and playback – so it sounds a lot better than the headphone jack on the Pi (or the no-headphone jack on a Pi Zero). We put ferrite beads and filter capacitors on every input and output to get the best possible performance, and all at a great price. We specifically designed this bonnet for use with making machine learning projects such as DIY voice assistants – for example, see this guide on creating a DIY Google Assistant.
When McDonald's spent over $300 million on big-data-crunching startup Dynamic Yield earlier this year, the move came as something of a surprise. Today the Golden Arches announced the acquisition of Apprente, a voice AI system focused on fast-food ordering. It's a niche, but it just paid off. Specific terms of the deal have not been disclosed. But the synergies are at least more immediately understandable.
A recent VUX World podcast took a deep dive into how voice AI is disrupting multiple industries. Special guest, Mike Zagorsek, VP of product marketing at SoundHound Inc. spoke with hosts Kane Simms and Dustin Coates of VUX World about the Houndify Voice AI platform. He highlighted how some of our partners (Mercedes-Benz, Pandora, and Mastercard) are using voice to create deeper relationships with their customers and extending the functionality and convenience of their products and services. The following is a recap of some of that conversation. You can watch and listen to the podcast in its entirety here.
Every day, Every Hour, Every Minute, we are saying few simple words like "Hey Google" or "Hey Alexa" or "Hey Siri" to know something or to get our works done. "Hey Google, How do you work?" or "Hey Alexa, Why are you so smart?" or "Hey Siri, What's behind your success?" It's simply because as a client we never bother about these things. In any application clients are the ones who needs to be satisfied and in today's world these assistants are so much smarter that there is no reason to think or ask these things as a client. But as a Tech Enthusiast, or as a guy from CS background it's always too much fascinating to know about behind the scene technologies, these popular companies are using to make these smart assistants capable of extraordinary performance.