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

Star Trek

2014 Concatenative
2020 NTTS

Song ID

Standard response
Music style response
Above are examples of how Alexa's voice has become more natural over the years.

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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Job summaryWould you like to work on a greenfield project that'll help improve the shopping experience of millions of Amazon customers? Want to help invent the next generation technologies in recommender and content optimization systems? We’ve got the perfect job for you.We are a team in a fast-paced organization with a huge impact on hundreds of millions of customers. We innovate at the intersection of customer experience, deep learning, and high-scale machine-learning systems.We are looking for Applied Scientists who love big data, and are capable of inventing and applying Machine Learning, Natural Language Processing, Image processing, Data Mining, Classification and Clustering techniques to solve real world problems and build novel customer facing innovations on Amazon. Our applied scientists work closely with software engineers to put algorithms into practice. They also work in partnership with teams across Amazon to create enormous benefits for our customers.As a member of the our Content Optimization team, you would be expected to move fast, have good judgment on what is and what is not worth exploring, create simple and scalable solutions and identify correct problem sets. You will be surrounded by thought-leaders in the Personalization space who are patent-leaders within Amazon. You will keep the team up-to date with latest academic research in relevant fields.About our team: Our team has the autonomy to decide where we can have the most impact and get down to experimenting. We love metrics and the fast pace. We analyze data to uncover potential opportunities, generate hypotheses, and test them. We refuse to accept constraints, internal or external, and have a strong bias for action. We imagine, build prototypes, validate ideas, and launch follow-up experiments from the successful ones.About our organization: Consider the following problem: every day, millions of customers with unique interests and needs come to Amazon looking for products out of a catalog of over a billion items. Not only do we need to decide what content would be most helpful to customers, we also need to present it in an inspiring manner. The Personalization organization within Amazon is responsible for the secret sauce that not only made Amazon the industry pioneer in building recommender systems at scale, but is also continuing to help raise the bar for building delightful and highly personalized shopping experiences.About you: You are an Applied Scientist with an interest in machine learning, data science, search, or recommendation systems. You have great problem solving skills. You love keeping abreast of the latest technology and use it to help you innovate. You have strong leadership qualities, great judgment, clear communication skills, and a track record of delivering great products. You enjoy working hard, having fun, and making history!
US, WA, Seattle
Job summaryAre you excited about using econometrics to make multi-million dollar decisions more Science and Data Driven? Are you interested in supporting Consumer Hardware device concepts from innovative idea inception to launch? Do you want to work on a Economics and Data Science team focused on tackling some of the hardest business questions within the Devices business at Amazon and then scaling those Statistics and Econometrics solutions via internal to Amazon tools? Then this could be the role for you!Amazon.com strives to be Earth's most customer-centric company where people can find and discover anything they want to buy online. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Amazon Devices and Services team is the area of Amazon focused on inventing platforms that delight customers by eliminating friction they have in supplying, entertaining, and managing the home and beyond.The Device Economics team owns demand estimates and pricing recommendations of concept devices before customers know they exist. We support over 100 device-specific analyses a year on hardware and services ranging from Echo Frames to Kindle Paperwhite to Blink Video Camera subscriptions to the Amazon Smart Plug…all prior to launch.. We are a cross-functional Product team working to scale Econometrics through Amazon and beyond by incorporating Science into internal facing tools and making it easier for others to do so as well.In this role, you will support up to senior leadership decision meetings around approving confidential funding requests (PRFAQs) for brand new devices and services, build decisions around how many hardware devices to manufacture prior to receiving any customer signal, and pricing decisions around how to price and promote products and services. You will leverage Science and Tools produced by the Device Economics team such as conjoint demand models to produce these recommendations. As part of the stakeholder-facing arm of the team, you will own relationships with decision makers to help improve the end-customer experience by making the decisions that impact those end-customers more data and Science-driven. In parallel, you will work with Scientists, Economists, Product Managers, and Software Developers to provide meaningful feedback about stakeholder problems to inform business solutions and increase the velocity, quality, and scope behind our recommendations. You will own projects to make progress on Decision Science itself. Through this all, we will invest in your development to pursue your career goals.We are willing to consider L5 candidates across the BA/BIE job families where we'll bar raise your Science skills.
US, WA, Seattle
Job summaryAlexa Smart Home Research is looking for a brilliant quantitative researcher to drive a new program to ensure Alexa always delivers a four star experience. In this role, you will define the roadmap for the SH segmentation program, create experiments to evaluate customer behavior and sentiment that drive these higher quality experiences for our target customers. These insights help Alexa Smart Home Marketing and Product teams make data driven decisions about our marketing and product strategies ensuring products are accurately conveyed, appropriately priced and designed, and with each launch we are moving the needle for customers to help them accomplish their ideal smart home.Key job responsibilities· Identify and propose key opportunities for improving the product development and marketing strategy for Alexa products· Develop and execute research projects, including leading all project phases: methodology and study design, data gathering and manipulation, analysis, interpretation and presentation of results· Lead and execute validation and impact studies· Define project requirements, document business and functional specifications, map current and future state business processes· Build automated mechanisms for evaluating, measuring, and deploying the algorithms and/or models you develop.· Bring a deep level of expertise in one of the Research Marketing disciplines (e.g. Statistics)A day in the lifeAs part of your work, you will lead quantitative research projects that build our understand of smart home customers, identifying what works well and areas of improvement for Alexa Smart Home that will ensure we continue to delight our customers. Excellent business and communication skills are a must to develop and define key business questions and to build data sets that answer those questions. You should be able to work with business customers in understanding the business requirements and research impact.About the teamWe are responsible for UX and market research (foundational, market fit, usability and concept testing), Beta launch readiness and voice of the customer. These services product org-wide customer insights that help SH teams connect directly with customers daily, supporting the end-to-end product readiness, and look around the corner to understand customer and competitor trends.
US, CA, Irvine
Job summaryWould you like to work on a greenfield project that'll help improve the shopping experience of millions of Amazon customers? Want to help invent the next generation technologies in recommender and content optimization systems? We’ve got the perfect job for you.We are a team in a fast-paced organization with a huge impact on hundreds of millions of customers. We innovate at the intersection of customer experience, deep learning, and high-scale machine-learning systems.We are looking for Applied Scientists who love big data, and are capable of inventing and applying Machine Learning, Natural Language Processing, Image processing, Data Mining, Classification and Clustering techniques to solve real world problems and build novel customer facing innovations on Amazon. Our applied scientists work closely with software engineers to put algorithms into practice. They also work in partnership with teams across Amazon to create enormous benefits for our customers.As a member of the our Content Optimization team, you would be expected to move fast, have good judgment on what is and what is not worth exploring, create simple and scalable solutions and identify correct problem sets. You will be surrounded by thought-leaders in the Personalization space who are patent-leaders within Amazon. You will keep the team up-to date with latest academic research in relevant fields.About our team: Our team has the autonomy to decide where we can have the most impact and get down to experimenting. We love metrics and the fast pace. We analyze data to uncover potential opportunities, generate hypotheses, and test them. We refuse to accept constraints, internal or external, and have a strong bias for action. We imagine, build prototypes, validate ideas, and launch follow-up experiments from the successful ones.About our organization: Consider the following problem: every day, millions of customers with unique interests and needs come to Amazon looking for products out of a catalog of over a billion items. Not only do we need to decide what content would be most helpful to customers, we also need to present it in an inspiring manner. The Personalization organization within Amazon is responsible for the secret sauce that not only made Amazon the industry pioneer in building recommender systems at scale, but is also continuing to help raise the bar for building delightful and highly personalized shopping experiences.About you: You are an Applied Scientist with an interest in machine learning, data science, search, or recommendation systems. You have great problem solving skills. You love keeping abreast of the latest technology and use it to help you innovate. You have strong leadership qualities, great judgment, clear communication skills, and a track record of delivering great products. You enjoy working hard, having fun, and making history!
US, WA, Seattle
Job summaryAre you excited about cutting-edge deep-learning NLP, NLU, and Conversational AI? If so, then come and join the Alexa Artificial Intelligence (AI) team. We are the science team behind Amazon’s intelligence voice assistance system and are responsible for the deep learning technology that is central to the automated ranking and arbitration to optimize for end-to-end customer satisfaction.Key job responsibilitiesAs an Applied Scientist, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art in spoken language understanding. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in spoken language understanding.A day in the life· Design, build, test and release predictive ML models· Ensure data quality throughout all stages of acquisition and processing, including such areas as data sourcing/collection, ground truth generation, normalization, and transformation.· Collaborate with colleagues from science, engineering and business backgrounds.· Present proposals and results to partner teams in a clear manner backed by data and coupled with actionable conclusions· Work with engineers to develop efficient data querying and inference infrastructure for both offline and online use casesAbout the teamWe are a science and engineering team part of Alexa AI organization. Our mission is to help Alexa decide which action to take in response to customer requests, incorporating a variety of contextual signals including both direct and indirect customer feedback to provide the best response to the customer. Our work directly contributes to improvement in Alexa business and customer metrics.