Goto

Collaborating Authors

speech recognition


Build your own voice assistant with this DIY kit

Mashable

With this coding kit, which is ideal for ages 11 and up, you can spend quality time with your kiddos creating Spencer, the DIY voice assistant. You'll follow step-by-step instructions to build Spencer. Don't worry, no prior coding or electronics knowledge is necessary. Along the way, you'll learn about coding a microcomputer in C, C, and CircuitBlocks (similar to Scratch), artificial intelligence, voice recognition, sound processing, soldering, and more. Once he's up and running, Spencer can tell you the weather forecast, sing a song, set alarms and reminders, show animations and scrolling text with flashy LEDs, find news online, and even tell jokes.


How AI Voice Assistants Can Revolutionize Health

#artificialintelligence

The vision for this future is to unlock the human voice as a meaningful measurement of health. AI voice assistants can transform speech into a vital sign, enabling early detection and predictions of oncoming conditions. Similar to how temperature is an indicator of fever, vocal biomarkers can provide us with a more complete picture of our health. One in four people globally will be affected by major or minor mental health issues at some point in their lives. Around 450 million people currently suffer from conditions such as anxiety, stress, depression, or others, placing mental health among the leading cause of ill-health worldwide.


How Do Cat Speech Translation Apps Work?

#artificialintelligence

You've probably seen apps that claim to translate what your cat is saying. But can they really translate your cat's meow into English? The short answer is yes, sort of. It's difficult because of how unique each cat's "language" is, but they can get pretty close with modern technology. Cat translation apps like MeowTalk use a form of speech recognition that emphasizes machine learning.


Voice Assistant Use Case Banking, Finance and Insurance Industries

#artificialintelligence

It is researched that 46 percent of US citizens use voice assistants. Observing the strong presence of voice assistants, banks, financial, service and insurance (BFSI) firms have actively adopted enterprise voice assistants for both internal (employees) and external (customers) purposes. It is said that JP Morgan & Co is enabling its clients by allowing access to research and analytics reports through voice chatbots. Also, twelve thousand field agents to be powered by voice assistant's capabilities, states Mark Madgett, the New York Life Insurances VP. Users can inquire about their account balance, latest transactions, fixed deposits, recurring deposits, loan balance, etc.


How Do Cat Speech Translation Apps Work?

#artificialintelligence

Over time, machine learning can turn speech recognition into a powerful tool. That's how speech recognition works for humans. But does it work for cats?


How Voice Assistants Boost Business Productivity - ONPASSIVE

#artificialintelligence

Seamless growth in Artificial Intelligence is redefining every component of enterprises operating around the globe. Things have become more comfortable for organizations as complex tasks are simplified and, retrieving and processing massive data volume has become effortless. As AI technology is evolving, AI-powered voice assistants are gaining a strong hold in the workplace. According to a report by Juniper Research, the number of devices that leverage voice assistants will be 8.4 billion by 2024, which will be more than the global population. Due to its ability in time management and enhancing productivity, businesses are eager to incorporate voice assistants in their workplace.


What are the Top 10 Voice AI Startups To Watch in 2021?

#artificialintelligence

A comprehensive list of top startup companies who are building quite a reputation in the tech domain through voice tech offerings. Voice AI has been around since IBM introduced it in 1961 through IBM Shoebox. It was the first digital speech recognition tool which at its time could recognize 16 words and 9 digits. Today, using voice AI, developers can train neural network models, create human like voices, chatbots and more. The voice AI tech startups space is booming and now encompasses various avenues such as voice analytics, speech recognition, artificial voice synthesis, voice transcription, voice recognition, among others.


The Advent Of Voice-First Computing & Connected Environment

#artificialintelligence

The real challenge of any innovation lies in the ability to resolve a set of logical functions as intended for the human longing nature of their expectations. Over the years like any other innovation, voice technology has also made its effective remark. In any consumer-facing technology, there always prevails a gap between the vision of the makers and the perception of the market, in that way the future of voice technology makes a significant bridging. Normally, voice recognition is the sense of a machine or program to understand and function according to the spoken words. The voice tech has come a long way from having to pronounce every single syllable to understand even the humming sound of us.


AI isn't yet ready to pass for human on video calls

#artificialintelligence

Leading up to Superbowl Sunday, Amazon flooded social media with coquettish ads teasing "Alexa's new body." Its gameday commercial depicts one woman's fantasy of the AI voice assistant embodied by actor Michael B. Jordan, who seductively caters to her every whim -- to the consternation of her increasingly irate husband. No doubt most viewers walked away giggling at the implausible idea of Amazon's new line of spouse replacement robots, but the reality is that embodied, humanlike AI may be closer than you think. Today, AI avatars -- i.e., AI rendered with a digital body and/or face -- lack the sex appeal of Michael B. Most, in fact, are downright creepy. Research shows that imbuing robots with humanlike features endears them to us -- to a point.


Deep learning - Wikipedia

#artificialintelligence

The word "deep" in "deep learning" refers to the number of layers through which the data is transformed. More precisely, deep learning systems have a substantial credit assignment path (CAP) depth. The CAP is the chain of transformations from input to output. CAPs describe potentially causal connections between input and output. For a feedforward neural network, the depth of the CAPs is that of the network and is the number of hidden layers plus one (as the output layer is also parameterized).