Alexa’s spoken-language-understanding research at Interspeech 2022

Methods for learning from noisy data, using phonetic embeddings to improve entity resolution, and quantization-aware training are a few of the highlights.

Interspeech, the world’s largest and most comprehensive conference on the science and technology of spoken-language processing, takes place this week in Incheon, Korea, with Amazon as a platinum sponsor. Amazon Science asked three of Alexa AI’s leading scientists — in the fields of speech, spoken-language-understanding, and text-to-speech — to highlight some of Amazon’s contributions to the conference.

Related content
Research from Alexa Speech covers a range of topics related to end-to-end neural speech recognition and fairness.

In this installment, senior principal scientist Gokhan Tur selects a few representative papers covering a wide range of topics in spoken-language understanding.

"Learning under label noise for robust spoken language understanding systems"

While deep-learning-based approaches have shown superior results for benchmark evaluation tasks, their performance degrades significantly when the training data is noisy. This is typically due to memorization, in which the model simply learns one-to-one correspondences between specific inputs and specific classifications, and the problem is especially acute for overparameterized models, which are already prone to overfitting. In this paper, the Alexa researchers perform a systematic study introducing various levels of controlled noise to the training data and explore five different label noise mitigation strategies for the task of intent classification:

  • Noise layer learns the noise distribution, adding a final layer to the model.
  • Robust loss uses both active loss (maximizing the probability of being in the labeled class) and passive loss (minimizing the probabilities of being in other classes).
  • LIMIT augments the objective function with the mutual information between model weights and the labels conditioned on data instances, to reduce memorization.
  • Label smoothing regularizes the model by replacing the hard 0 and 1 classification targets with smoothed values.
  • Early stopping aims to prevent overfitting by stopping when the validation error starts to increase.
Mitigation accuracies.png
The accuracy of various mitigation methods on public datasets. Top accuracy scores in bold.

The results table shows the effectiveness of these methods for the well-known language-understanding datasets ATIS, SNIPS, and TOP, for different noise levels. First, the researchers have shown that for each of the datasets, the accuracy of the baseline model (DistillBERT) has degraded more than 30%, with 50% noise level. The paper reports that all mitigation methods are effective in alleviating this degradation. The LIMIT approach performs best and is able to recover more than 80% of the dropped accuracy with 50% noise level and more than 96% with 20% noise level.

“Phonetic embedding for ASR robustness in entity resolution”

In Alexa, entity resolution (ER) is the task of retrieving the index of an entity given various ways of describing it in natural language. Phonetic variations are one big category of errors, such as “chip and potato” being recognized as “shipping potato”. While lexical and phonetic search methods are a straightforward way to resolve such errors, they are suboptimal since they cannot tell which pairs of phrases are more likely to be confused.

Related content
New model sets new standard in accuracy while enabling 60-fold speedups.

In this paper, Alexa researchers propose to employ phonetic embeddings based on the pronunciations of such phrases, where the similarity of pronunciation is directly reflected by the embedding-vector distance. Then they employ a neural vector search mechanism using a Siamese network to improve the robustness of the ER task against automatic speech recognition (ASR) noise. The phonetic embedding is combined with the semantic embedding from a pretrained BERT model. They also experimented with using the ASR n-best hypotheses as an input during training.

Weighted-sum model.png
The architecture of the weighted-sum model.

The paper presents results using the Video and Book domains in Alexa. In the evaluation of retrieval tests, the researchers see that, compared to the lexical-search baseline, the phonetic-embedding-based approach reduces the error rate by 44% in the Video domain and by 35% in the Book domain. With the ASR n-best data augmentation, they further reduce the error rate to 50% in the Video domain.

“Squashed weight distribution for low bit quantization of deep models”

Large deep-learning models — especially Transformer-based ones — have been shown to achieve state-of-the art performance on many public benchmark tasks. But their size often makes them impractical for real-world applications with memory and latency constraints. To this end, researchers have proposed various compression methods, such as pruning weights, distillation, and quantization.

Related content
Combination of distillation and distillation-aware quantization compresses BART model to 1/16th its size.

Quantization divides a variable’s possible values into discrete intervals, and maps all values in each interval to a single, representative value. It is a straightforward process with “bit-widths” of eight bits or more, meaning that each representative value has an eight-bit (or larger) index. It’s often applied after full-precision training of a model, but to avoid a mismatch between training and testing, researchers are turning to quantization-aware training approaches, where quantization noise is injected in the forward pass.

In this paper, Alexa researchers present the lowest reported quantization bit-widths for compressed Transformer models. They show only 0.2% relative degradation on public GLUE benchmarks with three-bit quantization and 0.4% relative degradation on Alexa data with only two-bit quantization. They achieve this with a reparameterization of the weights that squashes the distribution and by introducing a regularization term to the training loss to control the mean and variance of the learned model parameters.

The main idea is optimizing the overall distribution of weights under the well-known stochastic-gradient-descent (SGD) approach to training using a novel weight transformation that causes SGD to learn approximately uniformly distributed weights instead of the typical Gaussian distribution.

“Impact of acoustic event tagging on scene classification in a multi-task learning framework”

This paper explores the use of acoustic event tagging (AET) for improving the task of acoustic scene classification (ASC). Acoustic events represent information at levels of abstraction such as “car engine”, “dog-bark”, etc., while scenes are collections of acoustic events in no particular temporal order that represent information at higher levels of abstraction, such as “street traffic” and “urban park”. Previous studies suggest that humans leverage event information for scene classification. For instance, knowledge of the event “jet-engine” helps classify a given acoustic scene as “airport” instead of “shopping mall”.

Related content
Knowledge distillation technique for shrinking neural networks yields relative performance increases of up to 122%.

In this paper, Alexa researchers propose jointly training a deep-learning model to perform both AET and ASC, using a multitask-learning approach that uses a weighted combination of the individual AET and ASC losses. They show that this method lowers the ASC error rate by more than 10% relative to the baseline model and outperforms a model pretrained with AET first and then fine-tuned on ASC.

Multitask network.png
The ASC and AET baselines, along with the multitask network presented in the Amazon researchers’ paper.

“L2-GEN: A neural phoneme paraphrasing approach to L2 speech synthesis for mispronunciation diagnosis”

For machine learning models that help users learn English as a second language (ESL), mispronunciation detection and diagnosis (MDD) is an essential task. However, it is difficult to obtain non-native (L2) speech audio with fine-grained phonetic annotations. In this paper, Alexa researchers propose a speech synthesis system for generating mispronounced speech mimicking L2 speakers.

L2-GEN.png
The architecture of the L2-GEN framework.

The core of the system is a state-of-the-art Transformer-based sequence-to-sequence machine translation model. The L1 reference phoneme sequence of a word is treated as the source text and its corresponding mispronounced L2 phoneme sequences as "paraphrased" target texts. The researchers’ experiments demonstrate the effectiveness of the L2-GEN system in improving MDD accuracy on public benchmark evaluation sets.

Related content

US, WA, Seattle
We are a team of doers working passionately to apply cutting-edge advances in deep learning in the life sciences to solve real-world problems. As a Senior Applied Science Manager you will participate in developing exciting products for customers. Our team rewards curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the leading edge of both academic and applied research in this product area, you have the opportunity to work together with a diverse and talented team of scientists, engineers, and product managers and collaborate with others teams. Location is in Seattle, US Embrace Diversity Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and 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 Balance Work and Life Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives Mentor & Grow Careers Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future. Key job responsibilities • Manage high performing engineering and science teams • Hire and develop top-performing engineers, scientists, and other managers • Develop and execute on project plans and delivery commitments • Work with business, data science, software engineer, biological, and product leaders to help define product requirements and with managers, scientists, and engineers to execute on them • Build and maintain world-class customer experience and operational excellence for your deliverables
US, Virtual
The Amazon Economics Team is hiring Interns in Economics. 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 Stata, R, or Python is necessary. Experience with SQL, UNIX, Sawtooth, and Spark 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, 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 85% of interns from previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US, WA, Seattle
Amazon internships are full-time (40 hours/week) for 12 consecutive weeks with start dates in May - July 2023. Our internship program provides hands-on learning and building experiences for students who are interested in a career in hardware engineering. This role will be based in Seattle, and candidates must be willing to work in-person. Corporate Projects (CPT) is a team that sits within the broader Corporate Development organization at Amazon. We seek to bring net-new, strategic projects to life by working together with customers and evolving projects from ZERO-to-ONE. To do so, we deploy our resources towards proofs-of-concept (POCs) and pilot programs and develop them from high-level ideas (the ZERO) to tangible short-term results that provide validating signal and a path to scale (the ONE). We work with our customers to develop and create net-new opportunities by relentlessly scouring all of Amazon and finding new and innovative ways to strengthen and/or accelerate the Amazon Flywheel. CPT seeks an Applied Science intern to work with a diverse, cross-functional team to build new, innovative customer experiences. Within CPT, you will apply both traditional and novel scientific approaches to solve and scale problems and solutions. We are a team where science meets application. 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.
US, WA, Seattle
Amazon internships are full-time (40 hours/week) for 12 consecutive weeks with start dates in May - July 2023. Our internship program provides hands-on learning and building experiences for students who are interested in a career in hardware engineering. This role will be based in Seattle, and candidates must be willing to work in-person. Corporate Projects (CPT) is a team that sits within the broader Corporate Development organization at Amazon. We seek to bring net-new, strategic projects to life by working together with customers and evolving projects from ZERO-to-ONE. To do so, we deploy our resources towards proofs-of-concept (POCs) and pilot programs and develop them from high-level ideas (the ZERO) to tangible short-term results that provide validating signal and a path to scale (the ONE). We work with our customers to develop and create net-new opportunities by relentlessly scouring all of Amazon and finding new and innovative ways to strengthen and/or accelerate the Amazon Flywheel. CPT seeks an Applied Science intern to work with a diverse, cross-functional team to build new, innovative customer experiences. Within CPT, you will apply both traditional and novel scientific approaches to solve and scale problems and solutions. We are a team where science meets application. 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.
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 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. We’re seeking a Principal Scientist with a deep expertise in Search Science. Your responsibilities will include everything from developing and prototyping innovative machine learning, and deep learning algorithms to implementing, testing, and supporting full solutions in a production environment. We are looking for innovators who can contribute to advancing search technology on what’s scientifically possible while remaining committed to creating world-class products. Joining this team, you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon.com (AMZN), Earth's most customer-centric company 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. Key job responsibilities As a hands-on leader of this team, you’ll be responsible for defining key research questions, identifying relevant data, adopting or proposing innovative machine learning solutions conducting rigorous experiments, publishing results and working with the engineering team to deploy these solutions. As a strategic leader, you will identify investment opportunities, develop long term strategies, and propose, prioritize and deliver on goals. You’ll also participate in organizational planning, hiring, mentorship and leadership development. You will be technically fearless and with a passion for building scalable science and engineering solutions. You will serve as a key scientific resource in full-cycle development (conception, design, implementation, testing to documentation, delivery, and maintenance). About the team Starting in 2009, the Visual Search & Augmented Reality team has thus far launched many visual search solutions on the Amazon App that use computer vision and machine learning/deep learning to help customers complete their shopping missions more easily; multiple internal teams at Amazon (devices, Kindle, Seller services, etc.) also use our libraries and APIs to deliver solutions to their own customers. We are a full stack shop, and our team capabilities cover the whole solution spectrum, ranging across applied science, large scale engineering services, product management, UX design, and mobile app development for iOS and Android.
US, MN, Minneapolis
AWS Central Economics is an interdisciplinary team on the cutting edge of economics, statistical analysis, and machine learning whose mission is to solve problems that have high risk with abnormally high returns. Our team leverages the strengths of our scientists to build solutions for some of the toughest business problems here at Amazon AWS. We are looking for an exceptionally talented, seasoned, and motivated Economist to manage a team of economists and data scientists to drive the science for AWS. Key job responsibilities Manage a team of economists and data scientists to deliver actionable economic analyses to business leaders, provide leadership on the economics and science used in the analyses, and engage with business leaders to identify challenges AWS faces that call for in-depth economic analyses and to ensure the analyses have their intended impact.
LU, Luxembourg
&ltHire Relocation Requisition - not for posting> Provides insights to leadership on improving Supply Chain cost and Speed by using Data Science and Analytics techniques. Build Dashboards and models to industrialize these findings at scale.
US, VA, Arlington
The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. We are looking for economists who are able to work with business partners to hone complex problems into specific, scientific questions, and test those questions to generate insights. The ideal candidate will work with engineers and computer scientists to estimate models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into improved policies and programs at scale. We are looking for creative thinkers who can combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team. Ideal candidates will work closely with business partners to develop science that solves the most important business challenges. They will work in a team setting with individuals from diverse disciplines and backgrounds. They will serve as an ambassador for science and a scientific resource for business teams, so that scientific processes permeate throughout the HR organization to the benefit of Amazonians and Amazon. Ideal candidates will own the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. Key job responsibilities Use causal inference methods to evaluate the impact of policies on employee outcomes. Examine how external labor market and economic conditions impact Amazon's ability to hire and retain talent. Use scientifically rigorous methods to develop and recommend career paths for employees. A day in the life Work with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a DID analysis or estimate a structural model, or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions. About the team We are a multidisciplinary team that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer.
US, WA, Seattle
The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. We are looking for economists who are able to apply economic methods to address business problems. The ideal candidate will work with engineers and computer scientists to estimate models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into improved policies and programs at scale. We are looking for creative thinkers who can combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team. Ideal candidates will work in a team setting with individuals from diverse disciplines and backgrounds. They will work with teammates to develop scientific models and conduct the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. Key job responsibilities Use causal inference methods to evaluate the impact of policies on employee outcomes. Examine how external labor market and economic conditions impact Amazon's ability to hire and retain talent. Use scientifically rigorous methods to develop and recommend career paths for employees. A day in the life Work with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a DID analysis or estimate a structural model, or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions. About the team We are a multidisciplinary team that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer.
US, WA, Seattle
Amazon is looking for talented Postdoctoral Scientists to join our global Science teams for a one-year, full-time research position. Postdoctoral Scientists will innovate as members of Amazon’s key global Science teams, including: AWS, Alexa AI, Alexa Shopping, Amazon Style, CoreAI, Last Mile, and Supply Chain Optimization Technologies. Postdoctoral Scientists will join one of may central, global science teams focused on solving research-intense business problems by leveraging Machine Learning, Econometrics, Statistics, and Data Science. Postdoctoral Scientists will work at the intersection of ML and systems to solve practical data driven optimization problems at Amazon scale. Postdocs will raise the scientific bar across Amazon by diving deep into exploratory areas of research to enhance the customer experience and improve efficiencies. Please note: This posting is one of several Amazon Postdoctoral Scientist postings. Please only apply to a maximum of 2 Amazon Postdoctoral Scientist postings that are relevant to your technical field and subject matter expertise. Key job responsibilities * Work closely with a senior science advisor, collaborate with other scientists and engineers, and be part of Amazon’s vibrant and diverse global science community. * Publish your innovation in top-tier academic venues and hone your presentation skills. * Be inspired by challenges and opportunities to invent cutting-edge techniques in your area(s) of expertise.