Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    data analytics and truck accident claims
    How Data Analytics Reduces Truck Accidents and Speeds Up Claims
    7 Min Read
    predictive analytics for interior designers
    Interior Designers Boost Profits with Predictive Analytics
    8 Min Read
    image fx (67)
    Improving LinkedIn Ad Strategies with Data Analytics
    9 Min Read
    big data and remote work
    Data Helps Speech-Language Pathologists Deliver Better Results
    6 Min Read
    data driven insights
    How Data-Driven Insights Are Addressing Gaps in Patient Communication and Equity
    8 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Machine Learning Is The Latest Stage Of Text To Speech Technology
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Exclusive > Machine Learning Is The Latest Stage Of Text To Speech Technology
ExclusiveMachine Learning

Machine Learning Is The Latest Stage Of Text To Speech Technology

Matt James
Matt James
7 Min Read
text to speech with machine learning
Shutterstock Licensed Photo - By everything possible
SHARE

Machine learning has played a very important role in the development of technology that has a large impact on our everyday lives. However, machine learning is also influencing the direction of technology that is not as commonplace. AI text to speech technology is a prime example.

Contents
The Progression of Text to Speech Technology in the Machine Learning EraText to Speech – The Early DaysThe Machine Learning Technology That Drives TTSThe ChallengesMachine Learning is the Core of Speech to Text Technology

Text to speech technology predates machine learning by over a century. However, machine learning has made the technology more reliable than ever.

The Progression of Text to Speech Technology in the Machine Learning Era

We live in an era where audiobooks are gaining more appreciation than the traditional pieces of literature. Thus, it comes as no surprise that the Text-to-Speech (TTS) technology is also rapidly becoming popular. It caters to those who need it most, including children who struggle with reading, and those who suffer from a disability. Big data is very useful in assisting these people.

There are other elements of speech synthetization technology that rely on machine learning. It is now so sophisticated that it can even mimic someone else’s voice.

More Read

Image
6 Simple Steps to a Big Data Strategy
4 Key Ways Cannabis Marketers Can Use Big Data
The Cloud is Disrupting Project Management in the Construction Industry
Will Artificial Intelligence Render Human Transcriptionists Obsolete?
5 Tips to Improve the Data Security of Software Applications

Text to Speech (commonly known as TTS) is a piece of assistive technology (that is, any piece of technology that helps individuals overcome their challenges) that reads text out loud, and is available on almost every gadget we have on our hands today. It has taken years for the technology to develop to the point it is at today. Machine learning is changing the direction of this radical technology. However, its journey is one that started in the late eighteenth century.

Text to Speech – The Early Days

TTS is a complicated technology that has developed over a long period of time. It all began with the construction of acoustic resonators, which could only produce just the sounds of the vowels. These acoustics were developed in 1779, due to the dedicated work of Christian Kratzenstein. With the advent of semiconductor technology and improvements in signal processing, computer-based TTS devices started hitting the shelves in the 20th century. There was a lot of fascination surrounding the technology during its infancy. This was primarily why Bell Labs’ Vocoder demonstration found its way into the climactic scene of one of the greatest sci-fi flicks of all time – 2001: A Space Odyssey.

The Machine Learning Technology That Drives TTS

A couple of years ago, Medium contributor Utkarsh Saxena penned a great article on speech synthesis technology with machine learning. They talked about two very important machine learning approaches: Parametric TTS and Concatenative TTS. They both help with the development of new speech synthesizing techniques.

At the heart of it, a TTS engine has a front-end and a back-end component. Modern TTS engines are heavily dependent on machine learning algorithms. The front-end deals with converting the text to phonetics and meaningful sentences. The back-end uses this information to convert symbolic linguistic representation to sound. Good synthesizer technology is key to a good TTS system, which requires sophisticated deep learning neural analysis tools. The audio should be both intelligible and natural, to be able to mimic everyday conversation. Researchers are trying out various techniques to achieve this.

Concatenation synthesis relies on piecing together multiple segments of recorded speech to form coherent sentences. This technology usually gives way to the most natural-sounding speech. However, it loses out on intelligibility, leading to audible glitches as a result of poor segmentation. Formant synthesis is used when intelligibility takes precedence over natural language. This technology does not use human speech samples, and hence sounds evidently ‘robotic’. The lack of a speech-sample database means that it is relatively lightweight and best suited for embedded system applications. This is because power and memory resources are scarce in these applications. Various other technologies also exist, but the most recent and notable one is the use of machine learning. In fact, recorded speech data helps train deep neural networks. Today’s digital assistants use these extensively.

The Challenges

Contextual understanding of the text on the screen is one of the main challenges for TTS systems. More often than not, human readers are able to understand certain abbreviations without second thoughts. However, these are very confusing to computer models. A simple example would be to consider two phrases, “Henry VIII” and “Chapter VIII”. Clearly, the former should be read as Henry the Eighth and the latter should be read as Chapter eight. What seems trivial to us is anything but, for front-end developers working at TTS companies like Notevibes.

They use various predictive models to enhance the user experience. But there is a lack of standard evaluation criteria to judge the accuracy of a TTS system. A lot of variables go into the quality of a particular recording, and these variables are hard to control. This is due to the involvement of both analog and digital processing. However, an increasing number of researchers have begun to evaluate a TTS system based on a fixed set of speech samples.

That, in a nutshell (a rather big one at that), is an overview of Text to Speech systems. With increased emphasis on AI, ML, DL, etc., it is only a matter of time before we are able to synthesize true-to-life speech for use in our ever-evolving network of things.

Machine Learning is the Core of Speech to Text Technology

Machine learning is integral to the development of speech to text technology. New speech synthetization tools rely on deep neural algorithms to provide the highest quality outputs as this technology evolves.

TAGGED:machine learningMobile Technologyspeech to text
Share This Article
Facebook Pinterest LinkedIn
Share
ByMatt James
Matt James is a veteran marketer & tech geek that has helped many large brands increase their online footprint. He specializes in influencer outreach and business growth.

Follow us on Facebook

Latest News

data analytics and truck accident claims
How Data Analytics Reduces Truck Accidents and Speeds Up Claims
Analytics Big Data Exclusive
predictive analytics for interior designers
Interior Designers Boost Profits with Predictive Analytics
Analytics Exclusive Predictive Analytics
big data and cybercrime
Stopping Lateral Movement in a Data-Heavy, Edge-First World
Big Data Exclusive
AI and data mining
What the Rise of AI Web Scrapers Means for Data Teams
Artificial Intelligence Big Data Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

machine learning tools
Machine Learning

Can Online Machine Learning Tools Help with Personal Financial Management?

6 Min Read
machine learning helps with the testing process for mobile app development
Machine Learning

Machine Learning is Invaluable for Mobile App Testing Automation

9 Min Read
data quality and quantity in artificial intelligence
Artificial IntelligenceBig DataData QualityExclusiveMachine Learning

What To Know About The Impact of Data Quality and Quantity In AI

8 Min Read
8 examples of machine learning technology
Machine Learning

8 Revolutionary Applications Examples of Machine Learning in Real-Life

10 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

AI and chatbots
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence Chatbots Exclusive
ai is improving the safety of cars
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?