Alexa Scientists Present Two New Techniques That Improve Wake Word Performance

The Amazon Echo is a hands-free smart home speaker you control with your voice. The first important step in enabling a delightful customer experience with an Echo or other Alexa-enabled device is wake word detection, so accurate detection of “Alexa” or substitute wake words is critical. It is challenging to build a wake word system with low error rates when there are limited computation resources on the device and it's in the presence of background noise such as speech or music.

Next week, at the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2018, we are presenting two new techniques that improve on-device wake word detection performance:

  1. A new deep neural network (DNN) architecture for training a speech feature directly from raw audio input; and
  2. A novel background noise modeling method using monophone-based sound units that can take richer information into account.

In the first paper, we focus on improving the DNN-Hidden Markov Model (HMM) system by training a feature extraction DNN from raw audio rather than handcrafting a speech feature traditionally used in speech recognition. In the second paper, we present a new wake word system that comprises a two-stage classifier and show how wake word performance can be improved by incorporating richer phone (classes of sound) contexts into the two-stage system.

Time Delayed Bottleneck Highway Networks

The illustration below contrasts a conventional DNN architecture to our new DNN structure. A main difference between the two is that our new system replaces a handcrafted log-mel filter bank energy (LFBE) front-end with a trainable front-end DNN. By directly modeling raw audio rather than LFBE, we can learn novel features of the target wake word and optimize our classifier for improved performance. Except for the discrete Fourier transform (DFT), this approach is wholly data-driven. We apply the highway network to direct audio modeling to alleviate the hard optimization problem caused by a deep network structure. Furthermore, we efficiently reduce the large dimension of an input vector with a bottleneck layer followed by a time-delayed window.

Time Delayed Bottleneck Highway Networks

The graph below shows that our time-delayed bottleneck highway network with the DFT input significantly reduces a range of false alarm rates (FAR), yielding approximately a 20 percent relative improvement in the area under the curve (AUC), a common measure of machine-learning model accuracy. It is also clear from our work that a larger amount of training data would improve wake word detection performance.

Contrast of a conventional and our new wake word system
Contrast of a conventional and our new wake word system

Monophone-Based Background Modeling

In this paper, we introduce a two-stage on-device wake word detection system based on DNN acoustic modeling, propose a new approach for modeling background noise using monophone-based sound units, and present how richer information can be extracted from the monophone sound units to improve wake word accuracy.

An overview of the two-stage wake word system
An overview of the two-stage wake word system

With this new approach, we achieved about a 16 percent relative reduction in instances where Alexa doesn’t respond to the wake word (false rejection rates, or FRR) and about a 37 percent relative reduction in instances when Alexa mistakenly believes she’s heard the wake word, or false alarm rates (FAR). Moreover, when we introduce a second-stage classifier that extracts monophone units for final wake word detection, we reduce FAR by about 67 percent utilizing very few additional computational resources.

Below are the papers we’re presenting at ICASSP next week. Although each method is presented as separate work, both techniques can of course be combined to achieve better wake word performance. That will be the focus of our future work.

"Time-Delayed Bottleneck Highway Networks Using A DFT Feature For Keyword Spotting"
"Monophone-based Background Modeling For Two-Stage On-Device Wake Word Detection"

Acknowledgements: Kenichi Kumatani, Sankaran Panchapagesan, Jinxi Guo, Ming Sun, Anirudh Raju, Jiacheng Gu, Ryan Thomas, Nikko Ström, Shiv Naga Prasad Vitaladevuni, Bjorn Hoffmeister, Arindam Mandal, as well as the entire Wake Word team for supporting this work.

About the Author
Minhua Wu is an applied scientist in the Alexa Speech group.

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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 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
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.