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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MULTIPLE POSITIONS AVAILABLEEntity: Amazon Web Services, Inc., an Amazon.com CompanyTitle: Applied Scientist IILocation: East Palo Alto, CAPosition Responsibilities:Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy ML models on available data. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists.Amazon.com is an Equal Opportunity-Affirmative Action Employer - Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.#0000
US, WA, Seattle
We are a passionate team working to build a best-in-class healthcare product designed to make high-quality healthcare easy to access.We are looking for a truly innovative and technically strong applies scientist with a background in machine learning and natural language understanding.As a Senior Applied Scientist, you will:· develop models for various natural language processing tasks, including named-entity recognition, natural language inference, sentiment analysis, text summarization, and question answering within in a healthcare context· work closely with product managers to expand the depth of our product insights with data, create a variety of experiments, and determine the highest-impact projects to include in planning roadmaps· provide technical and scientific guidance to your team members· ensure that teams are collecting, understanding, and using data to inform every decision that impacts our customers· stay current with advancements and the latest modeling techniques in the field· publish your research findings in top conferences and journalsAbout You:· Problem Solver: Ability to utilize exceptional problem-solving skills to work through different challenges in ambiguous situations.· Doer: You’ve successfully delivered end-to-end AI/ML projects, working through conflicting viewpoints and data limitations.· Detail Oriented: You have an enviable level of attention to details, and catch things that others miss.· Communicator: Ability to communicate analytical results to senior leaders, peers, and external customers.· Influencer: Innovative scientist with the ability to identify opportunities in a fast-paced and ever-changing environment, and gain support with data and storytelling.Here at Amazon Care, we embrace our differences. We are committed to furthering our culture of inclusion. We have innovative benefit offerings, and we host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Our team also puts a high value on work-life balance. Striking a healthy balance between your personal and professional life is crucial to your happiness and success here, which is why we aren’t focused on how many hours you spend at work or online. Instead, we’re happy to offer a flexible schedule so you can have a more productive and well- balanced life—both in and outside of work.Amazon is committed to a diverse and inclusive workforce. Amazon is an equal opportunity employer and does not discriminate on the basis of race, ethnicity, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
US, NY, New York
Sponsored Products (SP) is Amazon's largest and fastest growing ad business. SP ads are shown prominently throughout search and product detail pages and allow shoppers to seamlessly discover products sold on Amazon. These are native ads that appear visually similar to other content on the page, which presents a huge opportunity for growth and impact, but also a significant responsibility to protect shopper experience.Job Responsibilities:· Design, develop, and deploy machine learning solutions.· Conduct hands-on data analysis, build large-scale machine-learning models and pipelines.· Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production.· Run regular A/B experiments, gather data, perform statistical analysis, and communicate the impact to senior leaders.· Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving.· Provide technical leadership, research new machine learning approaches to drive continued scientific innovation.· Be a member of the Amazon-wide Machine Learning Community, participating in internal and external MeetUps, Hackathons and Conferences.Impact and Career Growth:· Opportunity to grow and broaden your machine learning skills a make impact – the work you deliver directly impacts customers and revenue!· Work in an environment that thrives on creativity, experimentation, and product innovation.· Drive real-time algorithms to allocate billions of ads per day in advertising auctions.· Have the ability to experiment autonomously with meaningful projects.· Mentor others.Why you love this opportunity:Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities.
SG, Singapore
The Amazon Prime Team is hiring Interns in Economics. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to apply economic and econometric theories to large-scale business problems and big data sets.These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will work in a team of economists, data scientists, and engineers and in collaboration with product and finance managers. These experiences will translate well into writing applied chapters in your dissertation and prepare you with placement in academia or private sector.Roughly 50% of research assistants from previous cohorts have converted to full time data science or economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US, WA, Seattle
Interested in using AI to improve the shopping experience of millions of customers? Amazon Search has the perfect job for you.Amazon Search Customer Experience is looking for an experienced scientist to lead the innovation in Search Whole Page Optimization (WPO). Your research spans deep learning, reinforcement learning, and personalized recommendations. You will work with a team of scientists and engineers to make Amazon’s search experience intelligent, intuitive, and enjoyable.A successful candidate has strong customer obsession, highly-cited publications in relevant areas, and a track record of deploying research outcomes in production. You will bring deep technical expertise and strong business acumen. Amazon leaders are visionaries who are not afraid of rolling up their sleeves and getting their hands dirty. You will help shape the future of Amazon’s search customer experience by painting a compelling vision and leading the journey to get there. You must have the desire to make industry-wide impact and the ability to work within a fast moving environment to rapidly deliver innovations.As a senior leader, you will be responsible for the holistic optimization of Amazon search pages. From page layout to content ranking, from the navigation experience to product display optimization, you will rethink the assumptions behind traditional e-commerce experience and leverage AI to make the shopping journey of each customer a delightful one. You will be part of the Search technical leadership community that forms the backbone of the company. You will play a critical role in business planning, work closely with senior executives, and influence our long-term technical and business strategy.If you like the challenges and opportunities in this exciting space, come join us to work hard, have fun, and make history.
US, WA, Seattle
The Amazon Shipping is hiring Interns in Economics. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to apply what they've learned in an academic setting to a business environment, specifically focused on time series forecasting for routing problems.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, data scientists and MBAʼs. 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 50% of research assistants from previous cohorts have converted to full time data science or economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation
US, WA, Seattle
Amazon is looking for a creative Senior Research Scientist to tackle some of the most interesting problems on the leading edge of natural language processing (NLP), machine learning (ML), search and related areas with our Alexa AI team. Alexa AI aims to reinvent search and information retrieval for a voice-forward, multi-modal future. It enables customers to interact with unstructured and semi-structured content via a broad range of technologies including question answering, summarization, search, and multi-turn dialogues.If you are looking for an opportunity to solve deep technical problems and build innovative solutions in a fast-paced environment working within a smart and passionate team, this might be the role for you. 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 innovate, help move the needle for research in these exciting areas and build cutting-edge technologies that enable delightful experiences for hundreds of millions of people.In this role you will:· Work collaboratively with other scientists and developers to design and implement scalable models for accessing and presenting information;· · Drive scalable solutions from the business to prototyping, production testing and through engineering directly to production;· · Drive best practices on the team, deal with ambiguity and competing objectives, and mentor and guide junior members to achieve their career growth potential.