Abstract: Faced with the threat of identity leakage during voice data publishing, users are engaged in a privacy-utility dilemma when enjoying convenient voice services. Existing studies employ direct modification or text-based re-synthesis to de-identify users' voices, but resulting in inconsistent audibility in the presence of human participants. In this paper, we propose a voice de-identification system, which uses adversarial examples to balance the privacy and utility of voice services. Benefit from this, our system could preserve user identity from exposure by Automatic Speaker Identification (ASI) while remaining the voice perceptual quality for non-intrusive de-identification. Moreover, our system learns a compact speaker distribution through a conditional variational auto-encoder to sample diverse target embeddings on demand.

Faced with the threat of identity leakage during voice data publishing, users are engaged in a privacy-utility dilemma when enjoying convenient voice services. Existing studies employ direct modification or text-based re-synthesis to de-identify users' voices, but resulting in inconsistent audibility in the presence of human participants. In this paper, we propose a voice de-identification system, which uses adversarial examples to balance the privacy and utility of voice services. Instead of typical additive examples inducing perceivable distortions, we design a novel convolutional adversarial example that modulates perturbations into real-world room impulse responses. Benefit from this, our system could preserve user identity from exposure by Automatic Speaker Identification (ASI) while remaining the voice perceptual quality for non-intrusive de-identification. Moreover, our system learns a compact speaker distribution through a conditional variational auto-encoder to sample diverse target embeddings on demand. Combining diverse target generation and input-specific perturbation construction, our system enables any-to-any identify transformation for adaptive de-identification. Experimental results show that our system could achieve 98% and 79% successful de-identification on mainstream ASIs and commercial systems with an objective Mel cepstral distortion of 4.31dB and a subjective mean opinion score of 4.48.

Alexa is the natural language processing based system by Amazon. Alexa is the virtual assistant in products like Amazon Echo, Dot, Tap, FireTV and other third party products (there are 100 of these). The technology was first launched in 2012 and has now become an integral part of all of our lives. From kids have found someone who can help with their homework, to elderly who lean on Alexa as a reliable partner for reminding them of their medication to daily chores. For young professionals and busy moms it gives freedom to make lists, control smart homes etc with simply the use of voice commands.

Salesforce is shutting down two of its AI-powered voice services -- Einstein Voice Assistant and Voice Skills -- as it shifts resources toward its newly released Salesforce Anywhere app, as spotted by Voicebot.ai. A company spokesperson told VentureBeat that voice capabilities remain "a priority" for Salesforce, and that the products it's discontinuing will inform the development of "reimagined" functionality focused on productivity and collaboration. Einstein Voice Assistant, which launched in beta last year, was a component of Salesforce's Einstein Voice -- an outgrowth of the company's Einstein technology that enables customers to navigate cloud services hands-free. One of its ostensible advantages over other platforms was its versatility: It was siloed, restricting data pulls to individual users' accounts, and it could be "taught" to recognize jargon, acronyms, and slang in an organization's lexicon. Einstein Voice Assistant was more than a glorified transcriber.

Back in August 2019, the BBC made some waves with the news that it was developing a voice assistant called Beeb, an English language "Alexa" of its own that could interact with and control its array of radio and TV services, and its on-demand catalogue, and able to understand the array of accents you find in across the BBC's national footprint to boot. Ten months on, it's releasing its first live version of the service in the form of a beta to a select group of early adopters: UK-based members of the Windows Insider Program, a beta-testing, bug-seeking, early-adopter group popular in the Windows community, with over 10 million users globally. The idea with the limited release beta -- according to Grace Boswood, COO of BBC Design and Engineering -- will be to get Insiders to try out various features and stress test Beeb in the early beta, while at the same time giving the BBC a trove of usage data that can help it continue to train Beeb further, ahead of a wider release. The BBC is not naming a date yet for the general release. When you are a member in the UK, you have to be using the latest release of Windows 10, and then you download Beeb BETA form the Windows App Store.)

We are on the brink of entering a new technological era. A time where we will move beyond the need to use text interfaces to communicate with computers and instead, we will be talking to the devices themselves. With voice apps and voice assistants, devices will be able to understand and interact with us through voice technology. That's why voice discovery is going to challenge the established way of using smartphones to do most things online. In the future, most search queries will be done through voice commands, because using your voice to interact with a computer is simply the fastest way to get things done.

AI robots and self-driving cars might steal the headlines, but the next big leap in technology will be advances in voice services, according to Google's head of search, Ben Gomes, who says that a better understanding of common language is crucial to the future of the internet. "Speech recognition and the understanding of language is core to the future of search and information," said Gomes . "But there are lots of hard problems such as understanding how a reference works, understanding what'he', 'she' or'it' refers to in a sentence. It's not at all a trivial problem to solve in language and that's just one of the millions of problems to solve in language." Gomes was speaking to the Guardian ahead of Google's 20th anniversary on 24 September, more than seven years after Google launched its first voice service as simple speech-to-text for search. Now built into Google's search and its AI voice assistant which is embedded in billions of smartphones around the globe, voice recognition has become essential in developing countries with low literacy rates.

Voice controlled technologies are growing in popularity everywhere. From home automation to cab booking or ordering a meal, customers are getting pampered with the luxury of using voice to control things around them. In this blog post, I present a tutorial to build a voice-activated Oxford dictionary using Amazon Alexa. You can talk to the dictionary and ask for definitions, example usage, synonyms or antonyms of any English word present in the Oxford dictionary. Often I stumble across some word from a classic English literature.

In early 2014, Srikanth Thirumalai met with Amazon CEO Jeff Bezos. Thirumalai, a computer scientist who'd left IBM in 2005 to head Amazon's recommendations team, had come to propose a sweeping new plan for incorporating the latest advances in artificial intelligence into his division. He arrived armed with a "six-pager." Bezos had long ago decreed that products and services proposed to him must be limited to that length, and include a speculative press release describing the finished product, service, or initiative. Now Bezos was leaning on his deputies to transform the company into an AI powerhouse. Amazon's product recommendations had been infused with AI since the company's very early days, as had areas as disparate as its shipping schedules and the robots zipping around its warehouses. But in recent years, there has been a revolution in the field; machine learning has become much more effective, especially in a supercharged form known as deep learning. It has led to dramatic gains in computer vision, speech, and natural language processing. In the early part of this decade, Amazon had yet to significantly tap these advances, but it recognized the need was urgent. This era's most critical competition would be in AI--Google, Facebook, Apple, and Microsoft were betting their companies on it--and Amazon was falling behind.

How often has any piece of consumer technology had as eventful a year as 2017 has been for Amazon's Alexa voice service and Echo hardware? Consider the evidence: A year ago, Amazon boasted that there were 4,000 Alexa skills–tasks the service can perform, from setting a Nest thermostat to playing Jeopardy–up from 135 the previous January. Today, the count stands at 25,000. Alexa can now make phone and video calls, distinguish between the voices of multiple household members, and display information on screens, none of which it was able to do when the year began. In August, Amazon and Microsoft even announced that Alexa and Cortana would be able to talk to each other, a first-of-its-kind arrangement in the voice-assistant market.