Microsoft Announces Limited Access to Its Neural Text-to-Speech AI

The Custom Neural Voice is a Text-to-Speech (TTS) feature of Speech in Azure Cognitive Services that allows users to create a one-of-a-kind customized synthetic voice for their brand. Since the preview last year in September, the feature helped several customers such as AT&T, Duolingo, Progressive, and Swisscom to develop branded speech solutions for their customers. The feature is generally available (GA), yet access for customers to Custom Neural Voice includes technical controls to prevent misuse of the service – they have to apply for it.

Microsoft’s underlying Neural TTS technology for Custom Neural Voice consists of three major components: Text Analyzer, Neural Acoustic Model, and Neural Vocoder. The first component, Text Analyzer, is responsible for generating natural, synthetic speech from text. The text is first input into Text Analyzer, which provides output in the form of phoneme (a basic unit of sound that distinguishes one word from another in a particular language) sequence. Next, the phonemes sequence defines the pronunciations of the words provided in the text, which goes into the Neural Acoustic Model to predict acoustic features that define speech signals, such as the timbre, speaking style, speed, intonations, and stress patterns. And finally, the Neural Vocoder converts the acoustic features into audible waves to generate synthetic speech.

Neural TTS voice models are trained using deep neural networks based on real voice recording samples. With Custom Neural Voice’s customization capability, customers can adapt the Neural TTS engine to fit their user scenarios better. To leverage custom neural voice, customers will need an Azure account and subscription. Subsequently, after approval for using the feature, they can start a custom voice project, upload data, train, test, and deploy the voice model.

There are various use cases possible for customers to benefit from the Custom Neural Voice, such as customer service chatbots, voice assistants, online learning, audiobooks, public service announcements, and real-time translations. One earlier adopter, Swiss.com, wanted to create more engaging customer experiences by building a voice assistant that uniquely represents its brand. In a Microsoft Switzerland news item, the author wrote:

Using the Speech service, Swisscom has given its customers access to an intelligent, multilingual voice assistant, helping improve the customer experience and accelerate its own digital transformation.

Qinying Liao, principal program manager at Microsoft, described in an Azure AI blog post the benefit of leveraging Custom Neural Voice:

Empowered with this technology, Custom Neural Voice enables users to build highly-realistic voices with just a small number of training audios. This new technology allows companies to spend a tenth of the effort traditionally needed to prepare training data while at the same time significantly increasing the naturalness of the synthetic speech output when compared to traditional training methods.

In addition, Holger Mueller, principal analyst and vice president at Constellation Research Inc., told InfoQ:

In order to make computers more human, speech is a crucial ingredient, and in 2020 enterprises need to depart from the robotic and standardized voices, accents of synthetic speech in the past. The cloud enables this level of personalized creation of personalized voice experience - with availability, cheap compute, and operational capacity. So it is a widespread use case across the IaaS / PaaS players - and suitable for enterprises and their customers, and even employees as they get a more human experience.

Lastly, besides the capability to customize TTS voice models, Microsoft offers over 200 neural and standard voices covering 54 languages and locales.

#artificial intelligence #cloud #paas #microsoft azure #microsoft #ai #news

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Microsoft Announces Limited Access to Its Neural Text-to-Speech AI

The Custom Neural Voice is a Text-to-Speech (TTS) feature of Speech in Azure Cognitive Services that allows users to create a one-of-a-kind customized synthetic voice for their brand. Since the preview last year in September, the feature helped several customers such as AT&T, Duolingo, Progressive, and Swisscom to develop branded speech solutions for their customers. The feature is generally available (GA), yet access for customers to Custom Neural Voice includes technical controls to prevent misuse of the service – they have to apply for it.

Microsoft’s underlying Neural TTS technology for Custom Neural Voice consists of three major components: Text Analyzer, Neural Acoustic Model, and Neural Vocoder. The first component, Text Analyzer, is responsible for generating natural, synthetic speech from text. The text is first input into Text Analyzer, which provides output in the form of phoneme (a basic unit of sound that distinguishes one word from another in a particular language) sequence. Next, the phonemes sequence defines the pronunciations of the words provided in the text, which goes into the Neural Acoustic Model to predict acoustic features that define speech signals, such as the timbre, speaking style, speed, intonations, and stress patterns. And finally, the Neural Vocoder converts the acoustic features into audible waves to generate synthetic speech.

Neural TTS voice models are trained using deep neural networks based on real voice recording samples. With Custom Neural Voice’s customization capability, customers can adapt the Neural TTS engine to fit their user scenarios better. To leverage custom neural voice, customers will need an Azure account and subscription. Subsequently, after approval for using the feature, they can start a custom voice project, upload data, train, test, and deploy the voice model.

There are various use cases possible for customers to benefit from the Custom Neural Voice, such as customer service chatbots, voice assistants, online learning, audiobooks, public service announcements, and real-time translations. One earlier adopter, Swiss.com, wanted to create more engaging customer experiences by building a voice assistant that uniquely represents its brand. In a Microsoft Switzerland news item, the author wrote:

Using the Speech service, Swisscom has given its customers access to an intelligent, multilingual voice assistant, helping improve the customer experience and accelerate its own digital transformation.

Qinying Liao, principal program manager at Microsoft, described in an Azure AI blog post the benefit of leveraging Custom Neural Voice:

Empowered with this technology, Custom Neural Voice enables users to build highly-realistic voices with just a small number of training audios. This new technology allows companies to spend a tenth of the effort traditionally needed to prepare training data while at the same time significantly increasing the naturalness of the synthetic speech output when compared to traditional training methods.

In addition, Holger Mueller, principal analyst and vice president at Constellation Research Inc., told InfoQ:

In order to make computers more human, speech is a crucial ingredient, and in 2020 enterprises need to depart from the robotic and standardized voices, accents of synthetic speech in the past. The cloud enables this level of personalized creation of personalized voice experience - with availability, cheap compute, and operational capacity. So it is a widespread use case across the IaaS / PaaS players - and suitable for enterprises and their customers, and even employees as they get a more human experience.

Lastly, besides the capability to customize TTS voice models, Microsoft offers over 200 neural and standard voices covering 54 languages and locales.

#artificial intelligence #cloud #paas #microsoft azure #microsoft #ai #news

Microsoft Reveals Need To Prioritise Skills To Maximise Value From AI

Microsoft India today released new research revealing that organisations that combine the deployment of AI with skilling initiatives are generating most value from AI. The topline findings of the research underscore that mature AI firms are more confident about the return on AI and skills.

The tech giant recently conducted a global survey with approximately 12,000 people working with enterprise companies. The research surveyed employees and leaders within large enterprises across industry verticals in India, and 19 other countries, to look at the skills needed to thrive as AI becomes increasingly adopted by businesses, as well as the key learnings from early AI adopters.

The survey found a direct link between having the skills needed to thrive in an AI world and the value organisations gain from their AI implementations. The research further reveals that employees are keen to acquire AI relevant skills that are growing in importance and are of value to them personally and to the business. The organisation leaders surveyed predicted that half of all employees will be equipped with AI skills in the next 6-10 years, which is nearly one-and-a-half times more than the present estimations.

#news #ai research for businesses #ai survey #microsoft #microsoft ai for business survey #microsoft ai research #microsoft survey

Text to Speech in Node.js

In this video, I will be showing you how to turn text into speech in Node.js

#javascript #text to speech #javascript api #text to speech app #node.js text to speech #javascript text to speech

Navigating Between DOM Nodes in JavaScript

In the previous chapters you've learnt how to select individual elements on a web page. But there are many occasions where you need to access a child, parent or ancestor element. See the JavaScript DOM nodes chapter to understand the logical relationships between the nodes in a DOM tree.

DOM node provides several properties and methods that allow you to navigate or traverse through the tree structure of the DOM and make changes very easily. In the following section we will learn how to navigate up, down, and sideways in the DOM tree using JavaScript.

Accessing the Child Nodes

You can use the firstChild and lastChild properties of the DOM node to access the first and last direct child node of a node, respectively. If the node doesn't have any child element, it returns null.

Example

<div id="main">
    <h1 id="title">My Heading</h1>
    <p id="hint"><span>This is some text.</span></p>
</div>

<script>
var main = document.getElementById("main");
console.log(main.firstChild.nodeName); // Prints: #text

var hint = document.getElementById("hint");
console.log(hint.firstChild.nodeName); // Prints: SPAN
</script>

Note: The nodeName is a read-only property that returns the name of the current node as a string. For example, it returns the tag name for element node, #text for text node, #comment for comment node, #document for document node, and so on.

If you notice the above example, the nodeName of the first-child node of the main DIV element returns #text instead of H1. Because, whitespace such as spaces, tabs, newlines, etc. are valid characters and they form #text nodes and become a part of the DOM tree. Therefore, since the <div> tag contains a newline before the <h1> tag, so it will create a #text node.

To avoid the issue with firstChild and lastChild returning #text or #comment nodes, you could alternatively use the firstElementChild and lastElementChild properties to return only the first and last element node, respectively. But, it will not work in IE 9 and earlier.

Example

<div id="main">
    <h1 id="title">My Heading</h1>
    <p id="hint"><span>This is some text.</span></p>
</div>

<script>
var main = document.getElementById("main");
alert(main.firstElementChild.nodeName); // Outputs: H1
main.firstElementChild.style.color = "red";

var hint = document.getElementById("hint");
alert(hint.firstElementChild.nodeName); // Outputs: SPAN
hint.firstElementChild.style.color = "blue";
</script>

Similarly, you can use the childNodes property to access all child nodes of a given element, where the first child node is assigned index 0. Here's an example:

Example

<div id="main">
    <h1 id="title">My Heading</h1>
    <p id="hint"><span>This is some text.</span></p>
</div>

<script>
var main = document.getElementById("main");

// First check that the element has child nodes 
if(main.hasChildNodes()) {
    var nodes = main.childNodes;
    
    // Loop through node list and display node name
    for(var i = 0; i < nodes.length; i++) {
        alert(nodes[i].nodeName);
    }
}
</script>

The childNodes returns all child nodes, including non-element nodes like text and comment nodes. To get a collection of only elements, use children property instead.

Example

<div id="main">
    <h1 id="title">My Heading</h1>
    <p id="hint"><span>This is some text.</span></p>
</div>

<script>
var main = document.getElementById("main");

// First check that the element has child nodes 
if(main.hasChildNodes()) {
    var nodes = main.children;
    
    // Loop through node list and display node name
    for(var i = 0; i < nodes.length; i++) {
        alert(nodes[i].nodeName);
    }
}
</script>

#javascript 

This Week in AI | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.

#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai