Neural text-to-speech makes speech synthesizers much more versatile

A text-to-speech system, which converts written text into synthesized speech, is what allows Alexa to respond verbally to requests or commands. Through a service called Amazon Polly, text-to-speech is also a technology that Amazon Web Services offers to its customers.

Last year, both Alexa and Polly evolved toward neural-network-based text-to-speech systems, which synthesize speech from scratch, rather than the earlier unit-selection method, which strung together tiny snippets of pre-recorded sounds.

In user studies, people tend to find speech produced by neural text-to-speech (NTTS) systems more natural-sounding than speech produced by unit selection. But the real advantage of NTTS is its adaptability, something we demonstrated last year in our work on changing the speaking style (“newscaster” versus “neutral”) of an NTTS system.

At this year’s Interspeech, two new papers from the Amazon Text-to-Speech group further demonstrate the adaptability of NTTS. One is on prosody transfer, or synthesizing speech that mimics the prosody — shifts in tempo, pitch, and volume — of a recording. In essence, prosody transfer lets you choose whose voice you will hear reading back recorded content, with all the original vocal inflections preserved.

The other paper is on universal vocoding. An NTTS system outputs a series of spectrograms, snapshots of the energies in different audio frequency bands over short periods of time. But spectrograms don’t contain enough information to directly produce a natural-sounding speech signal. A vocoder is required to fill in the missing details.

A typical neural vocoder is trained on data from a single speaker. But in our paper, we report a vocoder trained on data from 74 speakers in 17 languages. In our experiments, for any given speaker, the universal vocoder outperformed speaker-specific vocoders — even when it had never seen data from that particular speaker before.

Our first paper, on prosody transfer, is titled “Fine-Grained Robust Prosody Transfer for Single-Speaker Neural Text-to-Speech”. Past attempts at prosody transfer have involved neural networks that take speaker-specific spectrograms and the corresponding text as input and output spectrograms that represent a different voice. But these tend not to adapt well to input voices that they haven’t heard before.

We adopted several techniques to make our network more general, including not using raw spectrograms as input. Instead, our system uses prosodic features that are easier to normalize.

First, our system aligns the speech signal with the text at the level of phonemes, the smallest units of speech. Then, for each phoneme, the system extracts prosodic features — such as changes in pitch or volume — from the spectrograms. These features can be normalized, which makes them easy to apply to new voices.

“But Germany thinks she can manage it … ”OriginalTransferredSynthesized
"I knew of old its little ways ... "OriginalTransferredSynthesized
“Good old Harry … ”OriginalTransferredSynthesized

Three different versions of the same three text excerpts. "Original" denotes the original recording of the text by a live speaker. "Transferred" denotes a synthesized voice with prosody transferred from the original recording by our system. And "Synthesized" denotes the synthesis of the same excerpt from scratch, using existing Amazon TTS technology.

This approach works well when the system has a clean transcript to work with — as when, for instance, the input recording is a reading of a known text. But we also examine the case in which a clean transcript isn’t available.

In that instance, we run the input speech through an automatic speech recognizer, like the one that Alexa uses to process customer requests. Speech recognizers begin by constructing multiple hypotheses about the sequences of phonemes that correspond to a given input signal, and they represent those hypotheses as probability distributions. Later, they use higher-level information about word sequence frequencies to decide between hypotheses.

When we don’t have reliable source text, our system takes the speech recognizer’s low-level phoneme-sequence probabilities as inputs. This allows it to learn general correlations between phonemes and prosodic features, rather than trying to force acoustic information to align with transcriptions that may be inaccurate.

In experiments, we find that the difference between the outputs of this textless prosody transfer system and a system trained using highly reliable transcripts is statistically insignificant.

Prosody_transfer_architecture.jpg._CB439112644_.jpg
The architecture of our prosody transfer system, both when speech transcripts are available (top left) and when they're not (top right). "Posteriograms" are sets of phonemic features predicted by an automatic speech recognition system.

Our second paper is titled “Towards Achieving Robust Universal Neural Vocoding”. In the past, researchers have used data from multiple speakers to train neural vocoders, but they didn’t expect their models to generalize to unfamiliar voices. Usually, the input to the model includes some indication of which speaker the voice belongs to.

We investigated whether it is possible to train a universal vocoder to attain state-of-the-art quality on voices it hasn’t previously encountered. The first step: create a diverse enough set of training data that the vocoder can generalize. Our data set comprised about 2,000 utterances each from 52 female and 22 male speakers, in 17 languages.

The next step: extensive testing of the resulting vocoder. We tested it on voices that it had heard before, voices that it hadn’t, topics that it had encountered before, topics that it hadn’t, languages that were familiar (such as English and Spanish), languages that weren’t (Ahmaric, Swahili, and Wolof), and a wide range of unusual speaking conditions, such as whispered or sung speech or speech with heavy background noise.

We compared the output of our vocoder to that of four baselines: natural speech, speaker-specific vocoders, and generalized vocoders trained on less diverse data — three- and seven-speaker data sets. Five listeners scored every output utterance of each vocoder according to the multiple stimuli with hidden reference and anchor (MUSHRA) test. Across the board, our vocoder outperformed the three digital baselines and usually came very close to the scores for natural speech.

Acknowledgments: Thomas Drugman, Srikanth Ronanki, Jonas Rohnke, Javier Latorre, Thomas Merritt, Bartosz Putrycz, Roberto Barra-Chicote, Alexis Moinet, Vatsal Aggarwal

Related content

US, WA, Seattle
The Global Media Entertainment Science team uses state of the art economics and machine learning models to provide Amazon’s entertainment businesses guidance on strategically important questions. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python is necessary, and experience with SQL and UNIX would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. Key job responsibilities
US, CA, Palo Alto
The Amazon Search team creates powerful, customer-focused search solutions and technologies. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, Amazon Product Search services go to work. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day. Our Search Relevance team works to maximize the quality and effectiveness of the search experience for visitors to Amazon websites worldwide. The Search Relevance team focuses on several technical areas for improving search quality. In this role, you will invent universally applicable signals and algorithms for training machine-learned ranking models. The relevance improvements you make will help millions of customers discover the products they want from a catalog containing millions of products. You will work on problems such as predicting the popularity of new products, developing new ranking features and algorithms that capture unique characteristics, and analyzing the differences in behavior of different categories of customers. The work will span the whole development pipeline, including data analysis, prototyping, A/B testing, and creating production-level components. Joining this team, you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon.com (AMZN), one of the world’s leading Internet companies. We provide a highly customer-centric, team-oriented environment in our offices located in Palo Alto, California. Please visit https://www.amazon.science for more information
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. Do you have a strong machine learning background and want to help build new speech and language technology? Amazon is looking for PhD students who are ready to tackle some of the most interesting research problems on the leading edge of natural language processing. We are hiring in all areas of spoken language understanding: NLP, NLU, ASR, text-to-speech (TTS), and more! A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will develop and implement novel scalable algorithms and modeling techniques to advance the state-of-the-art in technology areas at the intersection of ML, NLP, search, and deep learning. You will work side-by-side with global experts in speech and language to solve challenging groundbreaking research problems on production scale data. The ideal candidate must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Amazon has positions available for Natural Language Processing & Speech Intern positions in multiple locations across the United States. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. Please visit our website to stay updated with the research our teams are working on: https://www.amazon.science/research-areas/conversational-ai-natural-language-processing
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. The Research team at Amazon works passionately to apply cutting-edge advances in technology to solve real-world problems. Do you have a strong machine learning background and want to help build new speech and language technology? Do you welcome the challenge to apply optimization theory into practice through experimentation and invention? Would you love to help us develop the algorithms and models that power computer vision services at Amazon, such as Amazon Rekognition, Amazon Go, Visual Search, etc? At Amazon we hire research science interns to work in a number of domains including Operations Research, Optimization, Speech Technologies, Computer Vision, Robotics, and more! As an intern, you will be challenged to apply theory into practice through experimentation and invention, develop new algorithms using mathematical programming techniques for complex problems, implement prototypes and work with massive datasets. Amazon has a culture of data-driven decision-making, and the expectation is that analytics are timely, accurate, innovative and actionable. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Amazon Scientist use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science.
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. The Research team at Amazon works passionately to apply cutting-edge advances in technology to solve real-world problems. Do you have a strong machine learning background and want to help build new speech and language technology? Do you welcome the challenge to apply optimization theory into practice through experimentation and invention? Would you love to help us develop the algorithms and models that power computer vision services at Amazon, such as Amazon Rekognition, Amazon Go, Visual Search, etc? At Amazon we hire research science interns to work in a number of domains including Operations Research, Optimization, Speech Technologies, Computer Vision, Robotics, and more! As an intern, you will be challenged to apply theory into practice through experimentation and invention, develop new algorithms using mathematical programming techniques for complex problems, implement prototypes and work with massive datasets. Amazon has a culture of data-driven decision-making, and the expectation is that analytics are timely, accurate, innovative and actionable. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Amazon Scientist use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science.
CA, ON, Toronto
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. Are you a Masters student interested in machine learning, natural language processing, computer vision, automated reasoning, or robotics? We are looking for skilled scientists capable of putting theory into practice through experimentation and invention, leveraging science techniques and implementing systems to work on massive datasets in an effort to tackle never-before-solved problems. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science.
CA, ON, Toronto
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. Are you a PhD student interested in machine learning, natural language processing, computer vision, automated reasoning, or robotics? We are looking for skilled scientists capable of putting theory into practice through experimentation and invention, leveraging science techniques and implementing systems to work on massive datasets in an effort to tackle never-before-solved problems. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science.
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. We are looking for Masters or PhD students excited about working on Automated Reasoning or Storage System problems at the intersection of theory and practice to drive innovation and provide value for our customers. AWS Automated Reasoning teams deliver tools that are called billions of times daily. Amazon development teams are integrating automated-reasoning tools such as Dafny, P, and SAW into their development processes, raising the bar on the security, durability, availability, and quality of our products. AWS Automated Reasoning teams are changing how computer systems built on top of the cloud are developed and operated. AWS Automated Reasoning teams work in areas including: Distributed proof search, SAT and SMT solvers, Reasoning about distributed systems, Automating regulatory compliance, Program analysis and synthesis, Security and privacy, Cryptography, Static analysis, Property-based testing, Model-checking, Deductive verification, compilation into mainstream programming languages, Automatic test generation, and Static and dynamic methods for concurrent systems. AWS Storage Systems teams manage trillions of objects in storage, retrieving them with predictable low latency, building software that deploys to thousands of hosts, achieving 99.999999999% (you didn’t read that wrong, that’s 11 nines!) durability. AWS storage services grapple with exciting problems at enormous scale. Amazon S3 powers businesses across the globe that make the lives of customers better every day, and forms the backbone for applications at all scales and in all industries ranging from multimedia to genomics. This scale and data diversity requires constant innovation in algorithms, systems and modeling. AWS Storage Systems teams work in areas including: Error-correcting coding and durability modeling, system and distributed system performance optimization and modeling, designing and implementing distributed, multi-tenant systems, formal verification and strong, practical assurances of correctness, bits-IOPS-Watts: the interplay between computation, performance, and energy, data compression - both general-purpose and domain specific, research challenges with storage media, both existing and emerging, and exploring the intersection between storage and quantum technologies. As an Applied Science Intern, you will work closely with Amazon scientists and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment who is comfortable with ambiguity. Amazon believes that scientific innovation is essential to being the world’s most customer-centric company. Our ability to have impact at scale allows us to attract some of the brightest minds in Automated Reasoning and related fields. Our scientists work backwards to produce innovative solutions that delight our customers. Please visit https://www.amazon.science (https://www.amazon.science/) for more information.
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. We are looking for PhD students excited about working on Automated Reasoning or Storage System problems at the intersection of theory and practice to drive innovation and provide value for our customers. AWS Automated Reasoning teams deliver tools that are called billions of times daily. Amazon development teams are integrating automated-reasoning tools such as Dafny, P, and SAW into their development processes, raising the bar on the security, durability, availability, and quality of our products. AWS Automated Reasoning teams are changing how computer systems built on top of the cloud are developed and operated. AWS Automated Reasoning teams work in areas including: Distributed proof search, SAT and SMT solvers, Reasoning about distributed systems, Automating regulatory compliance, Program analysis and synthesis, Security and privacy, Cryptography, Static analysis, Property-based testing, Model-checking, Deductive verification, compilation into mainstream programming languages, Automatic test generation, and Static and dynamic methods for concurrent systems. AWS Storage Systems teams manage trillions of objects in storage, retrieving them with predictable low latency, building software that deploys to thousands of hosts, achieving 99.999999999% (you didn’t read that wrong, that’s 11 nines!) durability. AWS storage services grapple with exciting problems at enormous scale. Amazon S3 powers businesses across the globe that make the lives of customers better every day, and forms the backbone for applications at all scales and in all industries ranging from multimedia to genomics. This scale and data diversity requires constant innovation in algorithms, systems and modeling. AWS Storage Systems teams work in areas including: Error-correcting coding and durability modeling, system and distributed system performance optimization and modeling, designing and implementing distributed, multi-tenant systems, formal verification and strong, practical assurances of correctness, bits-IOPS-Watts: the interplay between computation, performance, and energy, data compression - both general-purpose and domain specific, research challenges with storage media, both existing and emerging, and exploring the intersection between storage and quantum technologies. As an Applied Science Intern, you will work closely with Amazon scientists and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment who is comfortable with ambiguity. Amazon believes that scientific innovation is essential to being the world’s most customer-centric company. Our ability to have impact at scale allows us to attract some of the brightest minds in Automated Reasoning and related fields. Our scientists work backwards to produce innovative solutions that delight our customers. Please visit https://www.amazon.science (https://www.amazon.science/) for more information.
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. Help us develop the algorithms and models that power computer vision services at Amazon, such as Amazon Rekognition, Amazon Go, Visual Search, and more! We are combining computer vision, mobile robots, advanced end-of-arm tooling and high-degree of freedom movement to solve real-world problems at huge scale. As an intern, you will help build solutions where visual input helps the customers shop, anticipate technological advances, work with leading edge technology, focus on highly targeted customer use-cases, and launch products that solve problems for Amazon customers. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. You will own the design and development of end-to-end systems and have the opportunity to write technical white papers, create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Amazon Scientist use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science