CAM: uninteresting speech detector

2020
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Abstract
Voice assistants such as Siri, Alexa, etc. usually adopt a pipeline to process users’ utterances, which generally include transcribing the audio into text, understanding the text, and finally responding back to users. One potential issue is that some utterances could be devoid of any interesting speech, and are thus not worth being processed through the entire pipeline. Examples of uninteresting utterances include those that have too much noise,are devoid of intelligible speech,etc. It is therefore desirable to have a model to filter out such useless utterances before they are ingested for downstream processing, thus saving system resources. Towards this end, we propose the Combination of Audio and Metadata (CAM) detector to identify utterances that contain only uninteresting speech. Our experimental results show that the CAM detector considerably outperforms using either an audio model or a metadata model alone, which demonstrates the effectiveness of the proposed system.
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US, CA, Palo Alto
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US, WA, Seattle
Job summaryWe are constantly making Alexa the best voice assistant in the world. Amazon’s Alexa cloud service and Echo devices are used every day, by people you know, in and about their homes. The Alexa Monetization team is hiring talented and experienced Sr. Applied Scientists to help building the next generation products for Alexa across multiple channels and domains. We are seeking an experienced, entrepreneurial, big thinker for a confidential new initiative within Alexa. You will be joining a team doing innovative work, making a direct impact to customers, showing measurable success, and building with the latest natural language processing systems. If you are holding out for an opportunity to:Make a huge impact as an individual· Be part of a team of smart and passionate professionals who will challenge you to grow every day· Solve difficult challenges using your expertise in coding elegant and practical solutions· Create applications at a massive scale used by millions of people· Work with machine learning systems to deliver real experiences, not just researchAnd you are experienced with…· Drive applied science (machine learning) projects end-to-end ~ from ideation, analysis, prototyping, development, metrics, and monitoring· Conduct deep analyses on massive user and contextual data sets· Propose viable modeling ideas to advance optimization or efficiency, with supporting argument, data, or, preferably, preliminary results· Design, develop, and maintain scalable, Machine Learning models with automated training, validation, monitoring and reporting· Stay familiar with the field and apply state-of-the-art Machine Learning techniques to NLP and related optimization problems· Produce peer-reviewed scientific paper in top journals and conferencesAnd you constantly look for opportunities to…· Innovate, simplify, reduce waste, and increase efficiencies· Use data to make decisions and validate assumptions· Automate processes otherwise performed by humans· Learn from others and help grow those around you...then we would love to chat!In 2021, we have the opportunity to build new products and features from the ground up and we are looking for strong, bias for action engineering leaders who are not afraid of taking bold bets and trying new things to improve customer experience for Alexa.As part of a new and growing team, you will be iterating on new features and products to help drive innovation and expansion. You will work on cross-functional and cross-domain opportunities; tackle challenging projects aim to accelerate experimentations in Alexa; and build out operating mechanisms and technology to enable novel customer experiences. You will be instrumental in setting the team culture, quality bar, engineering best practices, and norms. Mentoring and growing the team around you will be one of the primary ways you measure your own success. You will have the opportunity to contribute and develop deep expertise in the areas of distributed systems, machine learning, conversational technologies, user interfaces (including voice and natural user interfaces), data storage and data pipelines.This role is exciting for scientists who love to apply startup mindset to their day-to-day, enjoy working cross-functionally to master both business and technology knowledge, and are passionate about building engineering best practices. If you are looking for opportunity to learn, grow and lead, this is the position for you.
US, WA, Seattle
Job summaryWhy this job is awesome?· This is SUPER high-visibility work: Our mission is to provide consistent, accurate, and relevant delivery information to every single page on every Amazon-owned site.· MILLIONS of customers will be impacted by your contributions: The changes we make directly impact the customer experience on every Amazon site. This is a great position for someone who likes to leverage Machine learning technologies to solve the real customer problems, and also wants to see and measure their direct impact on customers.· We are a cross-functional team that owns the ENTIRE delivery experience for customers: From the business requirements to the technical systems that allow us to directly affect the on-site experience from a central service, business and technical team members are integrated so everyone is involved through the entire development process.· You will help the Delivery Experience organization to build causal inference framework and analyze the long-term effect on business· Your work will support the Amazon leadership to make visionary business decisions.· Do you want to join an innovative team of scientists and engineers who use machine learning and statistical inference techniques to deliver the best delivery experience on every Amazon-owned site?· · Are you excited by the prospect of analyzing and modeling terabytes of data on the cloud and create state-of-art algorithms to solve real world problems?· · Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company?· · Do you like to innovate and simplify?If yes, then you may be a great fit to join the Delivery Experience Machine Learning team.Major responsibilities:· Research and implement causal inference techniques to create scalable and effective models in Delivery Experience (DEX) systems· Solve business problems and identify business opportunities to provide the best delivery experience on all Amazon-owned sites.· Design and develop machine learning framework to measure the long-term effect of all models in DEX systems· Design and develop search ranking, recommendation and personalization models to improve Amazon customer experience· Analyze and understand large amounts of Amazon’s historical business data to detect patterns, to analyze trends and to identify correlations and causalities· Establishing scalable, efficient, automated processes for large scale data analysis and causal inference