How We Make Alexa Sound More Human-like Lead.png
Andrew Breen, senior manager, Amazon text-to-speech research, provided an overview of the history of TTS advancements at last June's re:MARS conference.

Advances in text-to-speech technologies help computers find their voice

Generating natural sounding, human-like speech has been a goal of scientists for decades.

Editor's Note: The Alexa team recently introduced a new longform speaking style so Alexa sounds more natural when reading long pieces of content, like this article. If you prefer to listen to this story rather than read it, below is this article utilizing the longform speaking style.

The spoken word is important to people. We love the sound of our child’s voice, of a favorite song, or of our favorite movie star reciting a classic line.

Computer-generated, synthesized spoken words also are becoming increasingly common. Alexa, Amazon’s popular voice service, has been responding to customers’ questions and requests for more than five years, and is now available on hundreds of millions of devices from Amazon and third-party device manufacturers. Other businesses also are taking advantage of computer-generated speech to handle customer service calls, market products, and more.

How we make Alexa sound more human-like

Language and speech are incredibly complex. Words have meaning, sure. So does the context of those words, the emotion behind them, and the response of the person listening. It would seem the subtleties of the spoken word would be beyond the reach of even the most sophisticated computers. But in recent years, advances in text-to-speech (TTS) technologies – the ability of computers to convert sequences of words into natural sounding, intelligible audio responses – have made it possible for computers to sound more human-like.

Amazon scientists and engineers are helping break new ground in an era where computers sound not only friendly and knowledgeable, but also predict how the sentiment of an utterance might sound to an average listener, for example, and respond with human-like intonations.

A revolution within the field occurred in 2016, when WaveNet – a technology for generating raw audio – was introduced. Created by researchers at London-based artificial intelligence firm DeepMind, the technique could generate realistic voices using a neural network trained with recordings of real speech.

Andrew Breen (crop)
Andrew Breen, senior manager, TTS research

“This early research suggested that a new machine learning method offered equal or greater quality and the potential for more flexibility,” says Andrew Breen, senior manager of the TTS research team in Cambridge, UK. Breen has long worked on the problem of making computerized speech more responsive and authentic. Before joining Amazon in 2018, he was director of TTS research for Nuance, a Massachusetts-based company that develops conversational artificial intelligence solutions.

Modeled loosely on the human neural system, neural nets are networks of simple but densely interconnected processing nodes. Typically, those nodes are arranged into layers, and the output of each layer passes to the layer above it. The connections between layers have associated “weights” that determine how much the output of one node contributes to the computation performed by the next.

Combined with machine learning, neural networks have accelerated progress in improving computerized speech. “It’s really a gold rush of invention,” says Breen.

Generating natural-sounding speech

Generating natural sounding, human-like speech has been a goal of scientists for decades. In the 1930s Bell Labs scientist Homer Dudley developed the Voder, a primitive synthetic-speech machine that an operator worked like a piano keyboard – except rather than music, out came a squawking mechanical voice. In the 1980s, a computerized TTS application called DECTalk, developed by the Digital Equipment Corporation, had progressed to the point where the late Stephen Hawking could use a version of it, paired with a keyboard to “talk”. The results were artificial-sounding, but intelligible words that many people still associated with a talking machine.

It's really been a gold rush of invention.
Andrew Breen, senior manager, TTS research

By the early 2000s, more accurate speech synthesis became common. The foremost approach taken then: hybrid unit concatenation. Amazon, for instance, used this approach until 2015 to build early versions of Alexa’s voice or to build voice capabilities into products like the Fire Tablet. Says Nikhil Sharma, a principal product manager in Amazon’s TTS group: “To create some of the early Alexa voices, we worked with voice talents in a studio for hours and had them say a wide variety of phrases. We broke that speech data down into a single diphone (a single diphone is a combination of halves of two phonemes, a distinct unit of sound) and put that in a large audio database. Then, when a request came to generate speech, we could tap into that database and select the best diphones to stitch together and create a sentence spoken by Alexa.”

Nikhil Sharma, principal product manager, TTS, Amazon
Nikhil Sharma, principal product manager, TTS

That process worked fairly well. But hybrid unit concatenation has its limits. It needs large amounts of pre-recorded sounds from professional voice talent for reference – sort of like a tourist constantly flipping through a large French book to find particular phrases. “Because of that, we really couldn’t say a hybrid unit concatenation system ‘learned’ a language,” says Breen.

Creating a computer that actually learns a language – not just memorizes phrases – became a goal of researchers. “That has been the Holy Grail, but nobody knew how to do it,” says Breen. “We were close but had a quality ceiling that limited its viability.”

Neural networks offered a way to do just that. In 2018, Amazon scientists demonstrated that by using a generative neural network approach to creating synthetic speech, they could produce natural sounding speech. Using the generative neural network approach, Alexa could also flex the way she speaks about certain content. For example, Amazon scientists created Alexa’s newscaster style of speech from just a few hours of training data, allowing customers to hear the news in a style to which they’ve become accustomed. This advance paved the way for Alexa and other Amazon services to adopt different speaking styles in different contexts, improving customer experiences.

Comparisons of Alexa synthesized speech

Below are examples of how Alexa's voice has become more natural over the years. Click to listen.

Alexa Star Trek 2014 Concatenative
Alexa Star Trek 2020 NTTS
Alexa Song ID Response Standard
Alexa Song ID Response Music Style

Amazon recently announced a new Amazon Polly feature called Brand Voice, which provides the opportunity for organizations to work with the Amazon Polly team of AI research scientists and linguists to build an exclusive, high-quality, neural TTS voice that represents their brand’s persona. Early adopters Kentucky Fried Chicken (KFC) Canada and National Australia Bank (NAB) have utilized the service to each create two unique brand voices that utilize the same deep learning technology that powers the voice of Alexa.

Amazon Polly is an AWS service that turns text into lifelike speech, allowing customers to build entirely new categories of speech-enabled products. Polly provides dozens of lifelike voices across a broad set of languages, allowing customers to build speech-enabled applications that work in many different countries.

Looking forward, Amazon researchers are working toward teaching computers to understand the meaning of a set of words, and speak those words using the appropriate affect. “If I gave a computer a news article, it would do a reasonable job of rendering the words in the article,” says Breen. “But it’s missing something. What is missing is the understanding of what is in the article, whether it’s good news or bad, and what is the focal point. It lacks that intuition.”

That is changing. Now, computers can be taught to say the same sentence with varying kinds of inflection. In the future, it’s possible they’ll recognize how they should be saying those words based simply on the context of the words, or the words themselves. “We want computers to be sensitive to the environment and to the listener, and adapt accordingly,” says Breen.

There are numerous potential TTS applications, from customer service and remote learning to narration of news articles. Driving improvements in this technology is one approach Amazon scientists and engineers are taking to create better experiences, not only for Alexa customers, but for organizations worldwide.

“The ability for Alexa to adapt her speaking style based on the context of a customer's request opens the possibility to deliver new and delightful experiences that were previously unthinkable,” says Breen. “These are really exciting times.”


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MULTIPLE POSITIONS AVAILABLEEntity: Amazon.com Services LLC, an Amazon.com companyTitle: Data Scientist IILocation: Seattle, WAPosition Responsibilities:Manage metrics reporting, perform data mining and big data analysis, to provide strategic advice on business and improve processes and systems. Collect business use cases, research and evaluate opportunities, and leverage data to support business functions through advanced mathematical modeling. Independently own and solve business problems. Transform business problems into mathematical models, provide data-driven solutions, and write clear and factually correct documents explaining technical concept to non-technical audience. Gather and use complex data sets across domains and proactively gather data when not readily available. Use specialized modelling software and scripting languages to scale methods. Partner with business, science, and engineering to establish scalable, efficient, automated processes for large scale data analyses, model development, model validation, and model implementation. Building and tuning machine learning models.Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000
US, WA, Seattle
Amazon's Choice uses machine-learning and big data to drastically simplify the shopping experience on Amazon, helping busy customers purchase high quality products that meet their needs, intuitively and with low risk of regret. By pioneering a recommendation system that picks only one product per need, it has struck a chord with customers, and is scaling rapidly as a result.Highlighting only one product is intrinsically harder than picking N products (what recommenders usually do). It creates new challenges as customers put more trust in each pick (so the cost of being wrong increases exponentially), and unlocks new opportunities as more surface area becomes available to address the nuances of each shopping request. The problem increases in complexity as Amazon's Choice highlights products in billions of shopping journeys each month across all marketplaces WW. This is a rich space for data science & analytics to look around corners, identify step-change opportunities to improve the accuracy of Amazon's Choice and customer experience, and use machine-learning techniques and statistical models to solve high impact complex science problems.As a Head of Data Science & Analytics for Amazon's Choice, you bridge the gap between science, tech and business. You lead a cross-functional team of data scientists, BI resources and data engineers, and partner with team's leadership and stakeholders (applied scientists, economists, software engineers and product managers) to design, implement and deliver ground-breaking data science solutions, such as a new machine learning model for a critical area of Amazon's Choice. You make sure your team has enough specificity to build the right solutions, and can discern which features are essential. You keep an eye for extensibility and scalability. You proactively “re-structure” data sources to unlock opportunities. You lead projects to improve data science and operational excellence best practices on your team. You are hands-on when need be, while being strategic about developing your talents. You are perpetually curious about new science techniques, and how to apply them in your space.