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Applications of Machine learning


Image recognition is one of the most common applications of machine learning. It is used to identify objects, persons, places, digital images, etc. Facebook provides us a feature of auto friend tagging suggestion. Whenever we upload a photo with our Facebook friends, then we automatically get a tagging suggestion with name, and the technology behind this is machine learning's face detection and recognition algorithm. It is based on the Facebook project named "Deep Face," which is responsible for face recognition and person identification in the picture: While using Google, we get an option of "Search by voice," it comes under speech recognition, and it's a popular application of machine learning. Speech recognition is a process of converting voice instructions into text, and it is also known as "Speech to text", or "Computer speech recognition."

Q&A: machine learning optimisation through speech recognition – Information Age


John Hughes, accuracy team lead at Speechmatics, spoke to Information Age about how machine learning optimisation can be achieved.

Machine Learning Research Engineer


Overview: Samsung Research America (SRA) plays a pivotal role in developing the next generation of discovery in software, user experience and services for future products that can enrich your life. Our mission is to research and develop new technologies by partnering with the best and brightest and creating a collaborative environment between industry and academia. Headquartered in Silicon Valley, with locations in many technology centers in North America, SRA is driven to build a culture of innovation that rapidly translates research and new ideas into the unexpected. We have the power to enrich lives. And that's what making a better global society is all about.

What is Artificial Intelligence and Machine Learning


One of the disruptive technologies that has gained increasingly more attention after the turn of the century is Machine Learning. Machine Leaning – closely related and usually considered as a subfield of Artificial Intelligence (AI) – is the process of automatic detection of usable patterns within data. The detection of these patterns is performed with the help of machine learning algorithms which are specifically tailored to deal with complex and large data sets. Such powerful algorithms have the potential of drastically revolutionizing the way of doing business and how businesses operate. With this article I will provide an overview of opportunities that machine learning algorithms and Artificial Intelligence (AI) pose to the business environment.

Quantifying the Uncertainty for Speech Recognition


Uncertainty measures how confident the model is in making its predictions [1]. The aleatoric type is a statistical uncertainty that appears due to an inherently random process. For example, the outcome of a coin flip has aleatoric uncertainty. The coin flip outcome is either heads or tails, either of which can occur with a 50% chance. The aleatoric type is an irreducible part of the uncertainty.

Dynamic Time Warping Algorithm in Time Series, Explained - KDnuggets


The phrase "dynamic time warping," at first read, might evoke images of Marty McFly driving his DeLorean at 88 MPH in the Back to the Future series. Alas, dynamic time warping does not involve time travel; instead, it's a technique used to dynamically compare time series data when the time indices between comparison data points do not sync up perfectly. As we'll explore below, one of the most salient uses of dynamic time warping is in speech recognition – determining whether one phrase matches another, even if the phrase is spoken faster or slower than its comparison. You can imagine that this comes in handy to identify the "wake words" used to activate your Google Home or Amazon Alexa device – even if your speech is slow because you haven't yet had your daily cup(s) of coffee. In time series analysis, Dynamic Time Warping (DTW) is one of the algorithms for measuring the similarity between two temporal time series sequences, which may vary in speed.

Create video subtitles with Amazon Transcribe using this no-code workflow


Subtitle creation on video content poses challenges no matter how big or small the organization. To address those challenges, Amazon Transcribe has a helpful feature that enables subtitle creation directly within the service. There is no machine learning (ML) or code writing required to get started. This post walks you through setting up a no-code workflow for creating video subtitles using Amazon Transcribe within your Amazon Web Services account. The terms subtitles and closed captions are commonly used interchangeably, and both refer to spoken text displayed on the screen.

Reduce Speech Transcription Costs by up to 90% with CAI (WP030)


Conversational artificial intelligence (CAI) uses deep learning (DL), a subset of machine learning (ML), to automate speech recognition, natural language processing and text to speech using machines.

36 Most Innovative Machine Learning Startups & Companies (Dublin, Ireland)


This article showcases our top picks for the best Dublin, Ireland based Machine Learning companies. These startups and companies are taking a variety of approaches to innovating the Machine Learning industry, but are all exceptional companies well worth a follow. We tried to pick companies across the size spectrum from cutting edge startups to established brands. AYLIEN is an artificial intelligence startup that focuses on creating technologies that help machines understand human languages better. SoapBox Labs privacy-first speech recognition software delivers voice-enabled experiences for kids of all ages, accents, and dialects.

Raising Robovoices

Communications of the ACM

In a critical episode of The Mandalorian, a TV series set in the Star Wars universe, a mysterious Jedi fights his way through a horde of evil robots. As the heroes of the show wait anxiously to learn the identity of their cloaked savior, he lowers his hood, and--spoiler alert-- they meet a young Luke Skywalker. Actually, what we see is an animated, de-aged version of the Jedi. Then Luke speaks, in a voice that sounds very much like the 1980s-era rendition of the character, thanks to the use of an advanced machine learning model developed by the voice technology startup Respeecher. "No one noticed that it was generated by a machine," says Dmytro Bielievtsov, chief technology officer at Respeecher.