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 senior principal 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.”

Research areas

Related content

US, VA, Arlington
As a Survey Research Scientist within the Reputation Marketing & Insights team, your primary responsibility will be to help manage our employee communications research program, including a global tracking survey. The work will challenge you to be resourceful, think big while staying connected to the details, translate survey, focus group results, and advanced analytics into strategic direction, and embrace a high degree of change and ambiguity at speed. The scope and scale of what we strive to achieve is immense, but it is also meaningful and energizing. This is an individual contributor role. The right candidate possesses endless curiosity and passion for understanding employee perceptions and what drives them. You have end-to-end experience conducting qualitative research, robust large-scale surveys, campaign measurement, as well as advanced modeling skills to uncover perception drivers. You have proficiency in diving deep into large amounts of data and translating research into actionable insights/recommendations for internal communicators. You are an excellent writer who can effectively communicate data-driven insights and recommendations through written documents, presentations, and other internal communication channels. You are a creative problem-solver who seeks to deeply understand the business/communications so you can tailor research that informs stakeholder decision making and strategic messaging tactics. Key job responsibilities - Design and manage the execution of a global tracking survey focused on employee communications - Develop research to identify and test messages to drive employee perceptions - Use advanced statistical methodologies to better understand the relationship between key internal communications metrics and other related measures of perception (e.g., regression, structural equation modeling, latent growth curve modeling, Shapley analysis, etc.) - Develop causal and semi-causal measurement techniques to evaluate the perception impact of internal communications campaigns - Identify opportunities to simplify existing research processes and operate more nimbly - Engage in strategic discussions with internal partner teams to ensure our research generates actionable and on-point findings About the team This team sits within the CCR organization. Our focus is on conducting research that identifies messaging opportunities and informs communication strategies for Amazon as a brand.
US, CA, Santa Clara
Want to work on frontier, world class, AI-powered experiences for health customers and health providers? The Health Science & Analytics group in Amazon's Health Store & Technology organization is looking for a Senior Manager of Applied Science to lead a group of applied scientists and engineers to work hand in hand with physicians to build the future of AI-powered healthcare experiences. We have an ambitious roadmap which includes scaling recently launched products which are already delighting products and the opportunity to build disruptive, new experiences. This role will be responsible for leading the science and technology teams driving these key innovations on behalf of our customers. Key job responsibilities - Independently manage a team of scientists and engineers to sustainably deliver science driven products. - Define the vision and long-term technical roadmap to achieve multi-year business objectives. - Maintain and raise the science bar of the team’s deliverables and keep the broader Amazon Health Services organization apprised of the latest relevant technical developments in the field. - Work across business, clinical, and technical leaders to disambiguate product requirements and socialize progress towards key goals and deliverables. - Proactively identify risks and shape the technical roadmap in anticipation of industry trends in emerging AI subfields.
US, NY, New York
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Senior Applied Scientist to work on pre-training methodologies for Generative Artificial Intelligence (GenAI) models. You will interact closely with our customers and with the academic and research communities. Key job responsibilities Join us to work as an integral part of a team that has experience with GenAI models in this space. We work on these areas: - Scaling laws - Hardware-informed efficient model architecture, low-precision training - Optimization methods, learning objectives, curriculum design - Deep learning theories on efficient hyperparameter search and self-supervised learning - Learning objectives and reinforcement learning methods - Distributed training methods and solutions - AI-assisted research About the team The AGI team has a mission to push the envelope in GenAI with Large Language Models (LLMs) and multimodal systems, in order to provide the best-possible experience for our customers.
US, WA, Seattle
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities - Develop ML models for various recommendation & search systems using deep learning, online learning, and optimization methods - Work closely with other scientists, engineers and product managers to expand the depth of our product insights with data, create a variety of experiments to determine the high impact projects to include in planning roadmaps - Stay up-to-date with advancements and the latest modeling techniques in the field - Publish your research findings in top conferences and journals A day in the life We're using advanced approaches such as foundation models to connect information about our videos and customers from a variety of information sources, acquiring and processing data sets on a scale that only a few companies in the world can match. This will enable us to recommend titles effectively, even when we don't have a large behavioral signal (to tackle the cold-start title problem). It will also allow us to find our customer's niche interests, helping them discover groups of titles that they didn't even know existed. We are looking for creative & customer obsessed machine learning scientists who can apply the latest research, state of the art algorithms and ML to build highly scalable page personalization solutions. You'll be a research leader in the space and a hands-on ML practitioner, guiding and collaborating with talented teams of engineers and scientists and senior leaders in the Prime Video organization. You will also have the opportunity to publish your research at internal and external conferences.
US, CA, San Francisco
If you are interested in this position, please apply on Twitch's Career site https://www.twitch.tv/jobs/en/ About Us: Twitch is the world’s biggest live streaming service, with global communities built around gaming, entertainment, music, sports, cooking, and more. It is where thousands of communities come together for whatever, every day. We’re about community, inside and out. You’ll find coworkers who are eager to team up, collaborate, and smash (or elegantly solve) problems together. We’re on a quest to empower live communities, so if this sounds good to you, see what we’re up to on LinkedIn and X, and discover the projects we’re solving on our Blog. Be sure to explore our Interviewing Guide to learn how to ace our interview process. You can work in San Francisco, CA or Seattle, WA. Perks - Medical, Dental, Vision & Disability Insurance - 401(k) - Maternity & Parental Leave - Flexible PTO - Amazon Employee Discount
IN, KA, Bengaluru
AWS Infrastructure Services owns the design, planning, delivery, and operation of all AWS global infrastructure. In other words, we’re the people who keep the cloud running. We support all AWS data centers and all of the servers, storage, networking, power, and cooling equipment that ensure our customers have continual access to the innovation they rely on. We work on the most challenging problems, with thousands of variables impacting the supply chain — and we’re looking for talented people who want to help. You’ll join a diverse team of software, hardware, and network engineers, supply chain specialists, security experts, operations managers, and other vital roles. You’ll collaborate with people across AWS to help us deliver the highest standards for safety and security while providing seemingly infinite capacity at the lowest possible cost for our customers. And you’ll experience an inclusive culture that welcomes bold ideas and empowers you to own them to completion. Do you love problem solving? Are you looking for real world Supply Chain challenges? Do you have a desire to make a major contribution to the future, in the rapid growth environment of Cloud Computing? Amazon Web Services is looking for a highly motivated, Data Scientist to help build scalable, predictive and prescriptive business analytics solutions that supports AWS Supply Chain and Procurement organization. You will be part of the Supply Chain Analytics team working with Global Stakeholders, Data Engineers, Business Intelligence Engineers and Business Analysts to achieve our goals. We are seeking an innovative and technically strong data scientist with a background in optimization, machine learning, and statistical modeling/analysis. This role requires a team member to have strong quantitative modeling skills and the ability to apply optimization/statistical/machine learning methods to complex decision-making problems, with data coming from various data sources. The candidate should have strong communication skills, be able to work closely with stakeholders and translate data-driven findings into actionable insights. The successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and ability to work in a fast-paced and ever-changing environment. Key job responsibilities 1. Demonstrate thorough technical knowledge on feature engineering of massive datasets, effective exploratory data analysis, and model building using industry standard time Series Forecasting techniques like ARIMA, ARIMAX, Holt Winter and formulate ensemble model. 2. Proficiency in both Supervised(Linear/Logistic Regression) and UnSupervised algorithms(k means clustering, Principle Component Analysis, Market Basket analysis). 3. Experience in solving optimization problems like inventory and network optimization . Should have hands on experience in Linear Programming. 4. Work closely with internal stakeholders like the business teams, engineering teams and partner teams and align them with respect to your focus area 5. Detail-oriented and must have an aptitude for solving unstructured problems. You should work in a self-directed environment, own tasks and drive them to completion. 6. Excellent business and communication skills to be able to work with business owners to develop and define key business questions and to build data sets that answer those questions 7. Work with distributed machine learning and statistical algorithms to harness enormous volumes of data at scale to serve our customers About the team Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve. Inclusive Team Culture AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. Mentorship and Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
US, NY, New York
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! We are looking for a self-motivated, passionate and resourceful Applied Scientist to bring diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. You will spend your time as a hands-on machine learning practitioner and a research leader. You will play a key role on the team, building and guiding machine learning models from the ground up. At the end of the day, you will have the reward of seeing your contributions benefit millions of Amazon.com customers worldwide. Key job responsibilities - Develop AI solutions for various Prime Video Search systems using Deep learning, GenAI, Reinforcement Learning, and optimization methods; - Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end; - Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses; - Effectively communicate technical and non-technical ideas with teammates and stakeholders; - Stay up-to-date with advancements and the latest modeling techniques in the field; - Publish your research findings in top conferences and journals. About the team Prime Video Search Science team owns science solution to power search experience on various devices, from sourcing, relevance, ranking, to name a few. We work closely with the engineering teams to launch our solutions in production.
US, WA, Bellevue
Are you interested in a unique opportunity to advance the accuracy and efficiency of Artificial General Intelligence (AGI) systems? If so, you're at the right place! As a Quantitative Researcher on our team, you will be working at the intersection of mathematics, computer science, and finance, you will collaborate with a diverse team of engineers in a fast-paced, intellectually challenging environment where innovative thinking is encouraged and rewarded. We operate at Amazon's large scale with the energy of a nimble start-up. If you have a learner's mindset, enjoy solving challenging problems, and value an inclusive team culture, you will thrive in this role, and we hope to hear from you. Key job responsibilities * Conduct statistical analyses on web-scale datasets to develop state-of-the-art multimodal large language models * Conceptualize and develop mathematical models, data sampling and preparation strategies to continuously improve existing algorithms * Identify and utilize data sources to drive innovation and improvements to our LLMs About the team We are passionate engineers and scientists dedicated to pushing the boundaries of innovation. We evaluate and represent the customer perspective through accurate benchmarking.
US, WA, Bellevue
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to help build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As an Applied Scientist with the AGI team, you will work with world-class scientists and engineers to develop novel data, modeling and engineering solutions to support the responsible AI initiatives at AGI. Your work will directly impact our customers in the form of products and services that make use of audio technology. About the team While the rapid advancements in Generative AI have captivated global attention, we see these as just the starting point. Our team is dedicated to pushing the boundaries of what’s possible, leveraging Amazon’s unparalleled ML infrastructure, computing resources, and commitment to responsible AI principles. And Amazon’s leadership principle of customer obsession guides our approach, prioritizing our customers’ needs and preferences each step of the way.
US, CA, Sunnyvale
The Artificial General Intelligence (AGI) team is looking for a highly skilled and experienced Senior Applied Scientist, to lead the development and implementation of algorithms and models for supervised fine-tuning and reinforcement learning through human feedback; with a focus across text, image, and video modalities. As a Senior Applied Scientist, you will play a critical role in driving the development of Generative AI (Gen AI) technologies that can handle Amazon-scale use cases and have a significant impact on our customers' experiences. Key job responsibilities - Collaborate with cross-functional teams of engineers, product managers, and scientists to identify and solve complex problems in GenAI - Design and execute experiments to evaluate the performance of different algorithms and models, and iterate quickly to improve results - Think big about the arc of development of GenAI over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems - Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports - Mentor and guide junior scientists and engineers, and contribute to the overall growth and development of the team