Breaking Through the Impossible
How we built revolutionary voice recognition technology at Amazon Alexa
Note: This is an excerpt from the upcoming book Building Alexa at Amazon: Innovating from Vision to Scale by Al Lindsay, a partner at Techquity and the founding VP of Engineering at Amazon Alexa. The book provides detailed lessons from scaling Alexa and how to solve hard technology problems to build breakout products that attract hundreds of millions of users.
When Jeff Bezos sent a simple one-paragraph email proposing a device that you could talk to from across the room, most experts in speech recognition deemed it impossible. Far-field voice recognition—the ability for computers to understand speech from a distance—was considered an intractable problem by most speech scientists. The physics were challenging: the signal-to-noise ratio decreases exponentially as the distance between the speaker and microphone increases. Add background noise, multiple voices, variable context, regional accents, and room geometry, and the far-field challenge seemed insurmountable.
When we introduced newly hired speech recognition scientists to the project and shared our goals, their typical response was, "That's impossible. It will never work."
Breaking Down the Impossible Into the Testable to Make it Doable
Yet the Alexa team wasn't deterred by this skepticism. Instead, we broke down the seemingly impossible problem into smaller, manageable pieces. We faced significant challenges in achieving sufficient Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU) accuracy. Initially, Alexa's word error rate was alarmingly high—misinterpreting 30 percent or more of words, making it nearly impossible to comprehend the speaker's intent from across the room.
Without a clear solution, our scientists and engineers conducted numerous experiments to improve the error rate. Jeff Bezos maintained relentless focus on this challenge, ending every meeting with the same question: "Where are we at with far-field speech recognition?" He recognized this was the primary invention risk that could sink the entire project.
One of the most significant breakthroughs came when we applied deep neural networks (DNNs) to the problem. While DNNs weren't entirely new, they required a large annotated dataset matching Alexa's target acoustic environment—people with different accents, genders, and ages asking about music or creating reminders in noisy household settings.
Alas, no such dataset existed. So we embarked on an ambitious and unconventional data collection project called AMPED. We rented houses and apartments across the country, set up audio collection equipment, and hired people to read scripts from tablets. The rooms were filled with everyday technology like televisions, and 20 Alexa devices positioned at varying heights, each with seven microphones to capture ambient sound.
We had a U-Haul to move all our equipment around. We were capturing so much data that we had to ship the drives back to Seattle or to our nearest data center to upload them into Amazon servers.
This massive effort spanned 12 cities, capturing full dialect and accent coverage for American English. The high-quality, far-field data collected through AMPED allowed us to train our models to recognize across-the-room speech in real-world conditions, cutting the error rate low enough to make Alexa workable.
Following this data collection initiative, we launched Alexa through an invitation program to early adopters, incrementally adding more users while constantly expanding our dataset and improving accuracy. This created a data collection flywheel that gave Amazon the largest collection of useful training data for far-field voice recognition globally.
The moral of this story? If you want to train a system to perform a new task, you need data that replicates the inputs and environments it will encounter during operations. That might cost money and require you to do scrappy, seemingly crazy things to break the physics of the problem.
When Alexa launched to the public, we had overcome what many experts had declared impossible. The success wasn't due to a single breakthrough but rather a combination of great people, relentless focus on the hardest problems, many experiments with incremental improvements, and unconventional approaches. Solving for far-field, however, was only half the battle. We had to make it useful for users.
Focus on Reducing User Friction
What made Alexa truly revolutionary, however, wasn't just solving the technical challenges of far-field speech recognition—it was how we reduced friction for users. Alexa improved the user interface for common tasks by eliminating the need to find a phone, unlock it, and navigate to an app. The ambient, always-ready voice interface was always available and extremely fast, making the complex as simple as just using your voice.
With Alexa, completing the task took one step, hands-free, with no 'touching glass.' You could perform this task even if you were rinsing dinner plates—or if you were literally juggling knives.
This friction reduction proved especially valuable for users with special needs and disabilities, who found that simply being able to control lights or adjust thermostats with voice commands significantly improved their quality of life.
Our focus on reducing friction extended to developers as well, with simple RESTful APIs allowing device makers to integrate Alexa services with just a few lines of code. This low-friction approach accelerated adoption, making Alexa the leading unified smart home platform.
Working Backwards: The Journey from an Email to Tens of Millions of Devices
Looking back, the journey from Jeff's simple email to one of the fastest-growing consumer products in history demonstrated to me, the team and the world that the impossible just takes a little longer. We missed our ship dates multiple times. In the end, we delivered something that took on a life of its own and remains a massive and vibrant community today. Clearly, the rewards were worth the time and energy required to get it just right.
As Alexa transformed from a seemingly impossible idea into a product used by hundreds of millions of people daily, it established a new normal. For kids, talking to inanimate objects and having them respond is simply expected. The story of Alexa reminds us that breakthrough innovation often comes from tackling the problems others dismiss as impossible. Amazon pioneered the way to do this by breaking down the impossible into tests and small progress, and by doing things that don't scale to get the data you need.
Al Lindsay is a partner at Techquity and a veteran technology leader and engineer with more than 30 years of experience leading teams that built and scaled complex systems. During his 15 years at Amazon, Al built and led the Alexa science and engineering teams, developing and commercializing the world’s first hyperscale hands-free ambient computing product. In addition to his work on Alexa, Al led the scale-out of the Amazon Prime technology organization as the product grew to 10 million subscribers. Throughout his career, Al has focused on building and leading high-performing teams, solving difficult technical problems and delivering innovative products.
We are thrilled to announce the upcoming release of a brand new e-book, Lessons from Alexa, written by Techquity partner, Al Lindsay! This book dives into the journey of building Amazon Alexa from an ambitious idea to a revolutionary product used by millions worldwide. Whether you're a tech enthusiast, product leader, or entrepreneur, we know the lessons shared will inspire and guide you.
Love the details on the process. Can’t wait to see more when the book comes out in full.