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Incheon, Korea
Conference
Interspeech 2022
September 18 - 22, 2022
Incheon, Korea

Overview

Interspeech is a technical conference focused on speech processing and application, emphasizing interdisciplinary approaches addressing all aspects of speech science and technology, ranging from basic theories to advanced applications.

Sponsorship Details

Platinum
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Related research
Booth B3
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Amazon organizing committee members

Accepted publications

Tutorials

Interspeech 2022 Tutorial on Self-supervised Representation Learning for Speech Processing Hybrid
September 18
This tutorial session will present self-supervised speech representation learning approaches and their connection to related research areas. Since many current methods focus solely on automatic speech recognition as a downstream task, we will review recent efforts on benchmarking learned representations to extend the application of such representations beyond speech recognition. A hands-on component of this tutorial will provide practical guidance on building and evaluating speech representation models.

Amazon speakers: Katrin Kirchhoff

Website: https://interspeech2022.org/program/tutorials.php
Interspeech 2022 Tutorial on Personalized Speech Enhancement: Data and Resource-Efficient Machine Learning
September 18
In this tutorial, we will explore various definitions of personalized speech enhancement in the literature and relevant machine learning concepts, such as zero- or few-shot learning approaches, data augmentation and purification, self-supervised learning, knowledge distillation, and domain adaptation. We will also see how these methods can improve data and resource efficiency in machine learning while achieving desired speech enhancement performance.

Amazon speaker: Minje Kim

Website: https://interspeech2022.org/program/tutorials.php

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Applied Scientist
US, WA, Bellevue
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Applied Science Manager, ATT
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Senior Applied Scientist, Amazon Search
US, WA, Seattle
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Intern - Economics, Weblab
US, NY, New York
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python is necessary, and experience with SQL and UNIX would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of 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. About the team Amazon's Weblab team enables experimentation at massive scale to help Amazon build better products for customers. A/B testing is in Amazon's DNA and we're at the core of how Amazon innovates on behalf of customers.
Applied Science Manager, Sponsored Products Detail Page Allocation and Pricing
US, WA, Seattle
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. As a core product offering within our advertising portfolio, Sponsored Products (SP) helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The SP team's primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! As an Applied Science Manager in Machine Learning, you will: Directly manage and lead a cross-functional team of Applied Scientists, Data Scientists, Economists, and Business Intelligence Engineers. Develop and manage a research agenda that balances short term deliverables with measurable business impact as well as long term investments. Lead marketplace design and development based on economic theory and data analysis. Provide technical and scientific guidance to team members. Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative and business judgment Advance the team's engineering craftsmanship and drive continued scientific innovation as a thought leader and practitioner. Develop science and engineering roadmaps, run annual planning, and foster cross-team collaboration to execute complex projects. Perform hands-on data analysis, build machine-learning models, run regular A/B tests, and communicate the impact to senior management. Collaborate with business and software teams across Amazon Ads. Stay up to date with recent scientific publications relevant to the team. Hire and develop top talent, provide technical and career development guidance to scientists and engineers within and across the organization. Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Team video ~ https://youtu.be/zD_6Lzw8raE
Sr. Applied Scientist, Search Science and AI
US, CA, Palo Alto
The Amazon Search team creates powerful, customer-focused search and advertising 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. Our Search Relevance team works to maximize the quality and effectiveness of the search experience for visitors to Amazon websites worldwide. Amazon’s large scale brings with it unique problems to solve in designing, testing, and deploying relevance models. We are seeking a strong applied Scientist to join the Experimentation Infrastructure and Methods team. This team’s charter is to innovate and evaluate ranking at Amazon Search. In practice, we aim to create infrastructure and metrics, enable new experimental methods, and do proof-of-concept experiments, that enable Search Relevance teams to introduce new features faster, reduce the cost of experimentation, and deliver faster against Search goals. Key job responsibilities You will build search ranking systems and evaluation framework that extend to Amazon scale -- thousands of product types, billions of queries, and hundreds of millions of customers spread around the world. As a Senior Applied Scientist you will find the next set of big improvements to ranking evaluation, get your hands dirty by building models to help understand complexities of customer behavior, and mentor junior engineers and scientists. In addition to typical topics in ranking, we are particularly interested in evaluation, feature selection, explainability. A day in the life Our primary focus is improving search ranking systems. On a day-to-day this means building ML models, analyzing data from your recent A/B tests, and guiding teams on best practices. You will also find yourself in meetings with business and tech leaders at Amazon communicating your next big initiative. About the team We are a team consisting of software engineers and applied scientists. Our interests and activities span machine learning for better ranking, experimentation, statistics for better decision making, and infrastructure to make it all happen efficiently at scale.
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