Why Alexa won't wake up when she hears her name in Amazon's Super Bowl ad

This Sunday's Super Bowl between the New England Patriots and the Los Angeles Rams is expected to draw more than 100 million viewers, some of whom will have Alexa-enabled devices within range of their TV speakers. When Amazon's new Alexa ad airs, and Forest Whitaker asks his Alexa-enabled electric toothbrush to play his podcast, how will we prevent viewers’ devices from mistakenly waking up?

With the Super Bowl ad — as with thousands of other media mentions of Alexa tracked by our team — we teach Alexa what individual recorded instances of her name sound like, so she will know to ignore them. We can also apply this technique, known as acoustic fingerprinting, on the fly to recognize when multiple devices from different households are hearing the same command at around the same time. This is crucial to preventing Alexa from responding to pranks on TV, references to people named Alexa, or other instances of her name in broadcast media that we don't know about in advance.

Our approach to matching audio recordings is based on classic acoustic-fingerprinting algorithms like that of Haitsma and Kalker in their 2002 paper “A Highly Robust Audio Fingerprinting System”. Such algorithms are designed to be robust to audio distortion and interference, such as those introduced by TV speakers, the home environment, and our microphones.

To produce an acoustic fingerprint, we first derive a grid of log filter-bank energies (LFBEs) for the acoustic signal, which represent the amounts of energy in multiple overlapping frequency bands in a series of overlapping time windows. The algorithm steps through the grid in two-by-two blocks and adds and subtracts the measurements in the grid cells in a standardized way. (Technically, it computes the 2-D gradient of each block.) The sign of the result — positive or negative — provides a one-bit summary of the values in the block. The summaries of all the blocks in the grid constitute the acoustic fingerprint, and two fingerprints are deemed to match if the fraction of bits that are different (the “bit error rate”) is small enough.

Acoustic-fingerprinting_figure.jpg._CB455311870_.jpg
An illustration of how fingerprints are used to match audio. Different instances of Alexa’s name result in a bit error rate of about 50% (random bit differences). A bit error rate significantly lower than 50% indicates two recordings of the same instance of Alexa’s name.

When we have audio samples in advance — as we do with the Super Bowl ad — we fingerprint the entire sample and store the result. With audio that’s streaming to the cloud from Alexa-enabled devices, we build up fingerprints piecemeal, repeatedly comparing them to other fingerprints as they grow.

If a match is found, the incoming request is ignored. Noisy audio may yield a match, but it requires the accumulation of more data (a larger fingerprint) than clean audio does.

Using this matching algorithm, we have built a system with multiple layers to protect customers at multiple stages:

  • On-device: On most Echo devices, every time the wake word “Alexa” is detected, the audio is checked against a small set of known instances where Alexa is mentioned in commercials. Due to the limits of device CPU, this set is generally restricted to commercials we expect to be currently airing on TV.
  • In the cloud: Every audio request to Alexa that starts with a wake word is checked in two ways:
    • Known media: the audio is checked against a large set of fingerprints for known instances of “Alexa” and other wake words in commercials and other media. These fingerprints can also make use of the audio that follows the wake word.
    • Unknown media: the audio is checked against a fraction of other Alexa requests arriving at around the same time. If the audio of a request matches that of requests from at least two other customers, we identify it as a media event. We also check incoming audio against a small cache of fingerprints discovered on the fly (the cached fingerprints are averages of the fingerprints that were declared matches). The cache allows Alexa to continue to ignore spurious wake words even when they no longer occur simultaneously.

Ideally, a device will identify media audio using locally stored fingerprints, so it does not wake up at all. If it does wake up, and we match the media event in the cloud, the device will quickly and quietly turn back off.

In addition to tracking new media mentions of Alexa’s name and updating our library of fingerprints accordingly, our team works continuously to improve the accuracy and efficiency of the fingerprinting system. We’re also exploring complementary technologies, such as machine learning systems that can distinguish media audio more generally from live human speech.

Acknowledgments: Joe Wang, Aaron Challenner, Mike Peterson, Michael Rudeen, Naresh Narayanan, Liangwei Guo, and the rest of the team

About the Author
Mike Rodehorst is a machine learning scientist in the Alexa Speech group.

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Job summaryThe Workforce Intelligence (WFI) team is looking for a Data Scientist to help us define and build the future of high-volume hiring analytics. You will play a critical role in the design and implementation of a strategic product for Amazon and help connect millions of applicants with professional opportunities at Amazon. You will be surrounded by people who are passionate about new technology trends, and believe that state-of-the-art product is critical to customer successKey job responsibilitiesWe are looking for a Data Scientist to join our rapidly growing team. As a Data Scientist, you can use a range of data science methodologies to solve challenging business problems when the solution is unclear. You have a combination of business acumen, broad knowledge of statistics, deep understanding of ML algorithms, and an analytical mindset. You thrive in a collaborative environment, and are passionate about learning. Our team has access to a variety of AWS tools such as Redshift, Sagemaker, Lambda, S3, and EC2 with a variety of skillsets in Tabular ML, NLP, Forecasting, Probabilistic ML and Causal ML. You will bring knowledge in many of these domains along with your own specialties and skillsets.A day in the lifeThe candidate will work closely with product and technical leaders throughout WFI and will be responsible for influencing technical decisions in areas of development/modelling that you identify as critical future product offerings. You will ensure ease of data-driven decision making for the team and, and build programs to raise the bar in predicting and tuning outcomes for customers.About the teamTeam:The vision of WFI is to design the ideal workforce to meet the customer promise anywhere. This organization leads and influences global workforce strategies that enable Amazon to scale more efficiently while also providing a unique voice for the hourly workforce.Work/Life HarmonyOur team puts a high value on work-life harmony. Striking a healthy balance between your personal and professional life is crucial to your happiness and success here, which is why we work with you to determine how to achieve that balance because we believe that one size does not fit all.Inclusive Team CultureHere at WFI, we embrace our differences. We are committed to furthering our culture of inclusion. We are constantly creating avenues that allow each one of us to be a part of Amazon’s employee led community groups. Within the team as well, we have an employee led group that leads efforts on employee engagement and activities. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Mentorship & Career GrowthOur 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, design discussions, and code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop.
CA, ON, Toronto
Job summaryAbout Amazon MusicAmazon Music reimagines music listening by enabling customers to unlock millions of songs and thousands of curated playlists and stations with their voice. Amazon Music provides unlimited access to new releases and classic hits across iOS and Android mobile devices, PC, Mac, Echo, and Alexa-enabled devices including Fire TV and more. With Amazon Music, Prime members have access to ad-free listening of 2 million songs at no additional cost to their membership. Listeners can also enjoy the premium subscription service, Amazon Music Unlimited, which provides access to more than 75 million songs and the latest new releases. Amazon Music Unlimited customers also now have access to the highest-quality listening experience available, with more than 75 million songs available in High Definition (HD), more than 7 million songs in Ultra HD, and a growing catalog of spatial audio. Customers also have free access to an ad-supported selection of top playlists and stations on Amazon Music. All Amazon Music tiers now offer a wide selection of podcasts at no additional cost, and live streaming in partnership with Twitch. Engaging with music and culture has never been more natural, simple, and fun. For more information, visit amazonmusic.com or download the Amazon Music app.The Music Growth team is focused on building a personalized, curated, and seamless music experience. We want to help our customers discover up-and-coming artists, while also having access to their favorite established musicians. We build systems that are distributed on a large scale, spanning our music apps, web player, and voice-forward audio engagement on mobile and Amazon Echo devices, powered by Alexa to support our customer base. Amazon Music offerings are available in countries around the world, and our applications support our mission of delivering music to customers in new and exciting ways that enhance their day-to-day lives.This role can be based in Toronto, Vancouver, New York, San Francisco, Seattle, Austin, Los Angeles or Atlanta.Key job responsibilitiesThe newly formed Customer Intelligence team's mission is to understand our customers intimately and identify their needs before they can identify it themselves. As the founding member of Customer Intelligece team, you will leverage your strong background in Computer Science and Machine Learning to help build the next generation of customer segments and propensity models. You will work closely with scientists, engineers, and stakeholders to build end-to-end ML solutions that have immediate impacts on Amazon and its customers. Your major responsibilities will include:• Identify trends in customer behavior using analytical and Data Science techniques• Develop NLP and deep learning models to extract insights from customer experiences and feedback• Use expertise to provide recommended architecture, scope, costs, and metrics to scale and automate Music Growth initiatives to a global audience• Design experiments and solutions for complex problem areas and business initiatives, and establish automated reporting using AWS technologies and Business Intelligence tools
US, NY, New York City
Job summaryAmazon AI is looking for an Applied Scientist to join ourscience team in the area of Speech and Natural Language Processing.Our organization develops the science that drives the cloud-based AIservices of AWS. Our mission is to put the power of AI into the handsof every developer. We seek to advance the state of the art in machinelearning to create services that delight our customers and meetreal-world business needs.As an Applied Scientist you will partner with talentedscientists and engineers to design, train, test, and deploy machinelearning models. You will contribute to innovative features, improveour services based on customer requirements and help maintain a highlyscalable data and model management infrastructure that supportscutting-edge research. You will be responsible for translatingbusiness and engineering requirements into deliverables and softwareproducts.We are looking for candidates who thrive in an exciting,fast-paced environment and who have a strong personal interest inlearning, researching, and creating new technologies with highcustomer impact.Prior domain knowledge in speech, natural language processing, orcomputer vision is strongly preferred; solid knowledge of fundamentalsof statistics, machine learning, and deep learning isrequired. Candidates should possess strong software engineeringskills and several years of industry experience.Inclusive Team CultureHere at AWS, 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.Work/Life BalanceOur 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.Mentorship & Career GrowthOur 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.
US, MA, Cambridge
Job summaryWant to transform the way people enjoy music, video, and radio? Come join the team that made Amazon Music, Spotify, Hulu, Netflix, Pandora, available to Alexa customers. We are innovating the way our customers interact with entertainment in the living room, on the go, and in the car. We are at the epicenter of the future of entertainment.Alexa Entertainment is looking for an Applied Scientist as we build a team of talented and passionate scientists for ASR (automatic speech recognition) and NLU (natural language understanding). As a Applied Scientist, you will participate in the design, development, and evaluation of models and ML (machine learning) technology so that customers have the magical experience of entertainment via Alexa. You will help lay the foundation to move from directed interactions to learned behaviors that enable Alexa to proactively take action on behalf of the customer. And, you will have the satisfaction of working on a product your friends and family can relate to, and want to use every day. Like the world of smart phones less than 10 years ago, this is a rare opportunity to have a giant impact on the way people live.You will be part of a team delivering features that are highly anticipated by media and well received by our customers.
US, CA, Santa Monica
Job summaryThe New Programs Group within Amazon Fashion is looking for Applied Scientist to help redefine and build new, exciting experiences for customers shopping for apparel and related fashion products. We own services that enable personalized customer experiences for our customers while pushing the envelope through novel scientific research, advancing the state of art in the field of Computer Vision and Machine Learning. We’re looking for a thought leader to lead the charge with cutting edge research and application of machine learning to address the unique and ambiguous problems in our space. You will work with talented peers in a team that provides opportunities to innovate in a start-up mode, partner with business teams to deliver customer experiences that disrupt status quo.The ideal candidate is a strong, creative and highly-motivated Scientist with hands-on experience in leading multiple research and engineering initiatives. You balance technical leadership with strong business judgment to make the right decisions about technology, tools, and methodologies. You excel in translating broader business objective into Machine Learning science formulations, research for potential solutions or invent new solutions for the objective. You strive for simplicity, demonstrate high judgment backed by sound statistical reasoning and robust machine learning models to deliver creative solutions. You do independent research and develop non-trivial Machine Learning solutions. You mentor and lead scientists and engineers, contribute to Amazon's Intellectual Property through patents and/or external publications.Position Responsibilities:· Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for applications of machine learning (Deep Learning, etc), optimization, simulation or visualization techniques.· Build and deploy scientific models on available data.· Research and implement novel approaches to add value to the business.· · Mentor junior engineers and scientists.To know more about Amazon science, Please visit https://www.amazon.science
US, WA, Bellevue
Job summaryCome join the Alexa Artificial Intelligence (AI) team, building the speech and language solutions behind Amazon Echo and other Amazon products and services! You will help us invent the future.As an Applied Scientist with the Alexa AI team, you will research and create models, and improve models for natural language processing and speech recognition problems. You will gain hands-on experience with Amazon’s heterogeneous structured data sources; as well as large-scale computing resources to accelerate advances in training deep neural networks for natural language understanding and automatic speech recognition on thousands of hours of speech. You will support solving highly visible and impactful business problems in areas of new product development, automation, self-service solution and quality improvement, to continue delight Alexa customers and help drive Amazon business performance.The ideal candidate should be passionate about delivering experiences that delight customers and creating solutions that are robust. They will also create reliable, scalable and high performance products that require exceptional technical expertise, and a sound understanding of the fundamentals of Machine Learning.