ICASSP: What “signal processing” has come to mean

Alexa scientist Ariya Rastrow on the blurring boundaries between acoustic processing and language understanding.

The International Conference on Acoustics, Speech, and Signal Processing (ICASSP), which starts today, is now in its 45th year, and according to Google Scholar’s rankings, it’s the highest-impact conference in the field of signal processing.

But as speech-related technologies have matured, the definition of signal processing has expanded. “ICASSP is a mix of a lot of different tracks,” says Ariya Rastrow, an Alexa principal research scientist who attended his first ICASSP in 2006. “It has the whole spectrum, from very low-level signal processing all the way to interpretation and natural-language understanding.”

Ariya Rastrow.png
Alexa principal research scientist Ariya Rastrow
Credit: Jordan Stead

This diversity, Rastrow explains, simply reflects that of the human audio-processing system. The brain doesn’t rely exclusively on acoustic signals to recognize words, and neither should computer systems.

“The interaction between language and acoustics is very dynamic from the human perspective,” Rastrow says. “If I’m talking to you in a very clean environment, we are capable of following on the acoustic level at very high resolution. But if we’re sitting in a noisy bar, you as a human are going to rely more on your prior — on a semantic level, what are the things that the other person might say? what are the topics that they might talk about? —and use that to enhance your recognition.”

Traditionally, the task of spoken-language understanding has been broken into two components: automatic speech recognition (ASR), which converts an acoustic speech signal into text, and natural-language understanding (NLU), which makes sense of the text.

But in fact, speech recognition usually relies on higher-level linguistic features to identify words. The traditional ASR system consists of an acoustic model, which translates acoustic signals into low-level phonetic representations; a lexicon, which maps sequences of low-level phonetic representations to words; and a language model, which uses high-level statistics about words’ co-occurrence to adjudicate between competing interpretations of the acoustic signal.

“Twenty, twenty-five years ago, there was this pragmatic idea to build factored systems,” Rastrow explains. “You have clear-cut boundaries between components of the system. Traditional speech recognition systems are built over an architecture that we call a hidden Markov model (HMM) architecture. The HMM architecture will put these multiple knowledge sources together at inference time. But the acoustic model and the language model are trained separately.”

Shared representations

Recently, however, this approach has begun to give way to end-to-end training of large, neural-network-based architectures. That is, a single neural network is trained on examples that consist of acoustic inputs and fully transcribed outputs, and it directly learns the relationships previously encoded in the ASR system’s separate components.

“This has many benefits” Rastrow says, “one being that by doing joint training you build systems that are more optimized in terms of accuracy. If you build factored systems, often you train each component for a specific objective function, and at inference time, they don’t know how to handle disfluencies and errors. By virtue of advances in architectures and doing joint training and multitask training, the systems are becoming more robust to those types of confusions.”

“That’s one benefit,” Rastrow continues. “Another is that the system gains in efficiency. By having a mechanism to do knowledge transfer, joint training, or shared representation, you get to the point where different parts of the systems can rely on the same types of representations or shared layers [of the network]. This can result in compression of the overall size of the system, execution speedups, and opportunities to deploy such systems on low-resource devices and hardware.

“For example, if you’re doing acoustic-event detection, and you’re also doing wake word detection and whisper detection, which are different types of audio-based classification tasks, one way is to build all the systems separately. The other way is that you can do knowledge transfer and shared representation learning, and by virtue of those shared network components and layers, you can gain efficiency beyond the obvious accuracy improvements.

“Also, the whole system is done in neural-network execution that we know how to accelerate both on the software and the hardware side, versus this explicit knowledge representation — lexicon versus language model. Traditionally, these are not deep-learning based, so we could not leverage these efficiency mechanisms. For the last two to three years, we have been pursuing this direction.”

Total integration

Allowing a single large model to integrate the ASR system’s low-level acoustic-signal processing and high-level language modeling raises the prospect of taking advantage of still higher-level linguistic features. In one of the 19 Amazon papers at this year’s ICASSP, for instance, Alexa researchers report using semantic features to help distinguish between utterances intended for Alexa and those that are not, where in the past, Alexa’s “device directedness” detector relied solely on acoustic features.

The end point of all this integration, of course, would be a single neural network that executed the entire task of spoken-language understanding — both ASR and NLU.

“There is emerging research that shows that at least for a subset of interactions, you can build a single, small-footprint network that can directly translate audio to the semantic level,” Rastrow says. “You get even better latency. You don’t have to do stage-wise execution. Also, there are studies showing that humans don’t do recognition word by word. We carry information on the parts of the speech that are semantically important for the topic, for the conversation.”

“But challenges remain,” Rastrow says. “These all-neural systems thrive on data. And once you move closer to the understanding layer, you have to cope more and more with data sparsity and the nuances of unique interactions. On the acoustic level, for the sound <p>, even across languages, you can get a lot of examples. But as you go closer to the semantic and sentence-level understanding, the patterns become more unique.

“One challenge is how we combine these new architectures for doing direct audio to NLU with our advances in semi-supervised learning and unsupervised learning. Another challenge is how to combine very data-oriented learning systems with some kind of reasoning or logic.

“I’ll give you an example. If you say, ‘Alexa turn on the bedroom light’, and Alexa misinterprets and turns on the kitchen light, and you follow that by saying, ‘No, Alexa, don’t turn on the kitchen light,’ now you have the negation problem. When you say ‘Don’t turn it on’, you really mean ‘Turn it off’. It is very hard to find those examples in data. Traditionally, we know how to address that problem with rules and logic and reasoning, but relying merely on data might not give us a good representation of those unique patterns. So the questions in the next two, three years of research will be how to combine those systems with either semi-supervised or unsupervised learning and how to combine them with knowledge and logic.”

About the Author
Larry Hardesty is the editor of the Amazon Science blog. Previously, he was a senior editor at MIT Technology Review and the computer science writer at the MIT News Office.

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Are you passionate about leveraging your data science skills to make impact at scale? Do you enjoy developing innovative algorithms, optimization and predictive models to generate insights and recommendations that will be used by millions of Amazon selling partners and FBA operation teams to drive customer impact?Over 2 million Sellers in 10 countries list their products for sale on the Amazon Marketplace. To meet our sellers’ needs, our smart and customer-obsessed employees are constantly innovating and building on new ideas. Fulfillment by Amazon (FBA) is an Amazon service for our sellers. FBA Inbound analytics and data science team partners with FBA inbound product management team to optimize supply chain cost and lead time variability by influencing right trade-offs among cost, speed and FBA supply network capacity.We are looking for a motivated Data Scientist to build, optimize and productionize cutting edge machine learning models. A successful candidate will have strong quantitative, data mining, statistical modeling, machine learning skills and is comfortable facilitating ideation and working from concept through to execution. The position will partner with Product Management, Engineering, Supply Chain optimization and Finance teams to enhance short term and long term business use cases that leverage a range of data science methodologies to solve complex problems for the global FBA Inbound network.A qualified candidate must have demonstrated ability to develop and manage medium to large-scale models and methodologies that are statistically grounded but also functional and practical. Must possess strong written and verbal communication skills, proven ability to engage and collaborate with customers to drive improvements. Possess high intellectual curiosity with ability to quickly learn new concepts/frameworks, algorithms and technology.Key responsibilities of FBA Inbound data scientist include the following:· Research machine learning algorithms and implement by tailoring to FBA business problems· Manipulate/mine data from large databases (Redshift, SQL Server) and create automated pipeline for model training data sets· Improve model usability by analyzing customer behavior and by gathering requirements from business owners and other tech teams.· Create and track accuracy and performance of model predictions/recommendations. Retrain models to maximize business impact· Foster culture of continuous engineering improvement through mentoring, feedback, and analysis.· Lead setting up of machine learning infrastructure and processes for team to collaborate and share codeTo help describe some of our challenges, we created a short video about Supply Chain Optimization at Amazon - http://bit.ly/amazon-scotAmazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation / Age
US, WA, Seattle
Amazon’s eCommerce Foundation (eCF) organization provides the core technologies that drive and power the Amazon website and the consumer experience. Millions of customer page views and orders per day are enabled by the systems eCF builds from the ground up. eCF Data enables business analytics and insights, providing data and data curation capabilities to thousands of internal and external customers worldwide.Amazon is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading machine learning services. As a scientist in the BDT team, you'll partner with technology and business teams to build services that surprise and delight our customers. You will be working with petabytes of structured and unstructured data to help our customers derive critical insights and solve real-world problems. You'll design and implement cutting-edge distributed ML services from the ground up, design and run experiments, research new algorithms, and find new ways of optimizing the risk, profitability, and customer experience for a wide variety of business segments across Amazon. As part of this group, you will have the chance to work with a large team of thought leaders, engineers, and scientists in the distributed computing, machine learning, and business intelligence fields.Applied science at Amazon is a fast growing field. This is a highly technical role that requires substantial cross-disciplinary interaction with software engineers, product managers, solution architects, business intelligence engineers, and other scientists. Besides theoretical analysis and innovation, you will work closely with software engineers to put your research, designs, and algorithms into practice. You will also work on cross-disciplinary efforts with other scientists and engineers at Amazon to establish scalable, efficient, automated processes for large-scale data analysis, ML model development, and model validation.We’re looking for top scientists capable of using ML, computer science, distributed systems, and other techniques to design, implement, and evangelize state-of-the-art solutions for previously-unsolved problems.
US, VA, Arlington
Global Talent Management (GTM) is a central Human Resource (HR) team responsible for creating and evolving Amazon’s human capital and talent products and processes. The GTM Science team is a growing interdisciplinary team within GTM that develops evidence-based products and services that power the growth and development of Amazon’s talent across all of our businesses and locations around the world.GTM Science exists to propel GTM and Amazon HR toward being the most scientific HR organization on earth. Our mission is to use science to assist and measurably improve every talent decision made at Amazon. We do this by discovering signals in workforce data, integrating those signals and behavioral recommendations into Amazon’s talent products, and helping Amazonians make high-judgement decisions that raise the bar on talent. Our multi-disciplinary approach covers an array of capabilities, including: data engineering, business intelligence and analytics, research and behavioral sciences, data science, and applied sciences such as economics and machine learning.We are looking for a dynamic leader to join our leadership team. Reporting directly to the Director of GTM Science, you will lead a team of researchers and analysts primarily responsible for handling high-priority senior-leadership research requests that require scientific rigor and agility. You will be responsible for building mechanisms to scale collaborations across all areas of GTM Science/Product/Tech and to build project-based partnerships with HR Line Analytics and COE teams. Your approach balances scientific rigor and pragmatism, in order to deliver results at the speed of business decision-making. You and your team thrive on quickly framing open-ended business requests into an actionable research plan and reporting your results to the highest levels of leadership, to meaningfully shape Talent processes, policies, and programs in areas such as: Diversity & Inclusion, Flexible Work, Talent Mobility, Talent Evaluation, Talent Retention, Performance Management.
US, CA, Sunnyvale
Want to build the future of music and audio entertainment?Imagine being part of an agile team, where your ideas have the potential to reach millions. Envision working within a startup atmosphere, while being able to leverage the resources of a Fortune-500 company. Picture working on bleeding-edge consumer-facing products, where every team member is a critical voice in the decision-making process. Welcome to Amazon Music’s New Projects team.Our team builds new experiences for Amazon Music listeners. We help our customers discover up-and-coming creators, while also having access to their favorite music and podcasts. We build systems that are distributed around the world, spanning our music apps, web player, and voice-forward experiences on mobile and Amazon Echo devices, powered by Alexa. Amazon Music products support our mission of delivering audio entertainment in new and exciting ways that listeners love.Amazon Music’s New Projects team is looking for founding team members across a variety of functions, including software engineering/development, product, marketing, design, and more. Come make history, as we launch new projects for millions of listeners.
US, MA, Boston
Data Scientist: Elastic Block Store Data Services (AWS)Come and build the future with us as we change the way customers move and transform their EBS data at never before seen scale.EBS customers frequently need to move or transform their underlying data whether its accelerating volume creation from snapshots using Fast Snapshot Restore, increasing their database storage or changing their volume type through Elastic volumes, or encrypting their volumes using AWS managed keys. Our team creates solutions that enable and simplify EBS customer workflows.Building a High-Performing & Inclusive Team CultureYou should be passionate about working with a world-class team that welcomes, celebrates, and leverages a diverse set of backgrounds and skillsets to deliver results. Driving results is your primary responsibility, and doing so in a way that builds on our inclusive culture is key to our long term success.Work/Life BalanceEBS Data Services values work-life balance. On normal days, our entire team is co-located in the Boston office, but we’re also flexible when people occasionally need to work from home. We generally keep core available hours from 10am to 4pm. Some of the team is available earlier and the rest of us work a little later.Energizing and Interesting Technical ProblemsYou will work in partnership with engineers on the team to build and operate large scale systems that move and transform customer volume data and accelerate access to their data. You’ll be working to provide solutions to both internal and external customers and engage deeply with other teams within EBS, S3, EC2, and many other services. It’s humbling and energizing to provide data movement solutions to customers at AWS scale.Mentorship & Career GrowthWe’re committed to the growth and development of every member of EBS Data Services, and that includes our engineers. You will have the opportunity to contribute to the culture and direction of the entire EBS org and deliver initiatives that will improve the life of all of our teams.EBS Data Services is a growth environment - we’re hiring and scaling rapidly to meet the needs of our customers. You’ll have the opportunity to grow your scope of influence naturally as we scale and work on solutions that impact some of Amazon's largest customers.
US, MA, Virtual Location - Massachuset
Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun.The Research and Advanced Development team at Amazon Robotics is seeking interns with a passion for robotic research to work on cutting edge algorithms for robotics. Our team works on challenging and high-impact projects, including allocating resources to complete a million orders a day, coordinating the motion of thousands of robots, autonomous navigation in warehouses, and learning how to grasp all the products Amazon sells. We are seeking internship candidates with backgrounds in computer vision, machine learning, resource allocation, discrete optimization, search, and planning/scheduling. You will be challenged intellectually and have a good time while you are at it!