Whisper to Alexa, and She’ll Whisper Back

If you’re in a room where a child has just fallen asleep, and someone else walks in, you might start speaking in a whisper, to indicate that you’re trying to keep the room quiet. The other person will probably start whispering, too.

Cozy in his crib
Image of a sleeping child, from the slide deck that Amazon senior vice president Dave Limp used when announcing whisper mode.
PeopleImages/Getty Images/iStockphoto

We would like Alexa to react to conversational cues in just such a natural, intuitive way, and toward that end, Amazon last week announced Alexa’s new whisper mode, which will let Alexa-enabled devices respond to whispered speech by whispering back. (The U.S. English version will be available in October.)

At the IEEE Workshop on Spoken Language Technology, in December, my colleagues and I will present a paper that describes the techniques we used to enable whisper mode. The ultimate implementation differs somewhat, but the basic principles are the same.

Whispered speech is predominantly unvoiced, meaning that it doesn’t involve the vibration of the vocal cords, and it has less energy in lower frequency bands than ordinary speech. Previously, researchers have sought to exploit these facts by training their classifiers, not on raw speech signals, but on “features” extracted from the signals, which are designed to capture information that could help discriminate whispers from normal speech.

In our paper, we compare the performance of two different neural nets on the whisper detection task. One is a relatively simple, feed-forward network known as a multilayer perceptron (MLP), and the second is a more sophisticated long short-term memory (LSTM) network.

The models are trained on two categories of features. One is log filter-bank energies, a fairly direct representation of the speech signal that records the signal energies in different frequency ranges. The other is a set of features specifically engineered to exploit the signal differences between whispered and normal speech.

We found that an LSTM network that doesn’t use handcrafted features performs as well as an MLP that does, indicating that LSTMs are capable of learning which signal attributes are most useful for whisper detection. In the paper, we also report an experiment in which the LSTM received the handcrafted features as well as the log filter-bank energies, and its performance improved still further.

After the paper’s acceptance, however, we found that the more data the LSTM saw, the less of an improvement the handcrafted features provided, until the difference evaporated. So the model we moved into production doesn’t use the handcrafted features at all.

There are several advantages to this approach. One is that other components of Alexa’s speech recognition system rely solely on log filter-bank energies. Using the same inputs for different components makes the system as a whole more compact, which is crucial if it is to be used offline, as we envision it will be.

Another advantage is that the handcrafted features are tailored to the data that we’ve seen so far. One of the features we used in our paper, for instance, is the ratio of the energy in the 6,875- to 8,000-hertz frequency band to that in the 310- to 620-hertz band. But it might be that, as we see more training data from more diverse populations, we find that ratios of energies in different frequency bands work better. A network that can learn features on its own is more scalable and can adapt more readily to new data.

LSTMs are widely used in speech recognition and natural-language understanding because they process inputs in sequential order, and their judgments about any given input are conditioned by what they’ve already seen.

This can pose a problem for whisper detection, however. In our system, before passing to the LSTM, the input utterance is broken into overlapping 25-millisecond segments called “frames”, which the LSTM processes in sequence. Because the LSTM’s output for a given frame reflects its outputs for the preceding frames, its confidence in its classifications tends to increase as the utterance progresses.

In a process called “end-pointing”, however, Alexa recognizes the end of an utterance by the short period of silence that follows end of speech, and that silence is part of the input to the whisper detector. When we apply the detector to live data, we typically see that its confidence increases across most of the duration of an utterance then falls off precipitously in the final 50 or so frames.

A graph of our whisper detector’s confidence in its classification (y-axis), across the duration of a single utterance (x-axis)
A graph of our whisper detector’s confidence in its classification (y-axis), across the duration of a single utterance (x-axis)

In the experiments reported in the paper, we tried to solve this problem in several different ways. One was to average the LSTM’s outputs for the entire utterance; one was to drop the last 50 frames and average what was left; and the third was to drop the last 50 frames and average only the preceding 100 frames, when the LSTM’s confidence should be at its peak.

Unexpectedly, averaging the entire signal — including the troublesome final 50 frames — yielded the best results. We suspect, however, that that’s because the samples of whispered speech that we used in our experiments were manually segmented, while the samples of normal speech were automatically segmented, using Alexa’s production end-pointer. There could be some consistent difference between manual and automatic segmentation that the system was actually exploiting to distinguish the two types of input, and dropping the final 50 frames made that difference more difficult to detect.

Nevertheless, in production, where both whispered speech and normal speech are segmented by the end-pointer, we’ve found that dropping the final 50 frames of data is crucial to maintaining performance and that averaging across a subset of the preceding frames, rather than the whole remaining signal, yields the best results.

Acknowledgments: Kellen Gillespie, Chengyuan Ma, Thomas Drugman, Jiacheng Gu, Roland Maas, Ariya Rastrow, Björn Hoffmeister

About the Author
Zeynab Raeesy is a speech scientist in the Alexa Speech group.

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Are you customer-obsessed, data oriented, and confident in proposing opportunities to improve our customers’ experience across different Amazon businesses? Amazon is looking for an experienced, talented and highly motivated individual to join our Customer Experience Strategy team! We are seeking a Research Scientist with a passion for NLP and Machine Learning model creation. You will be part of the team that adds new capabilities with multiple visual / audio analytic methodologies. The solutions heavily leverage AI and quantitative analysis to challenge conventional wisdom with hard data.As technology is advancing in unprecedented pace, Amazon Customer Experience Strategy is also evolving with cutting-edge engineering solutions. Our mission is to build end to end products and measure the customer experience of Amazon digital products around the world. We are a group of people who are inspired by inventions every day. We are obsessed with better customer experience for Prime Video, Alexa, Twitch… and the list goes on.Wait, how do we automate all the customer experience measurement? How do we know our products are faster / smoother / smarter? Well, here is a shortlist of technologies we’ve immersed ourselves into regularly.· Speech recognition and natural language (NLP) understanding· Computer Vision, including both Open CV and our own deep learning models.· Robotics and automation· Your role is to study the cutting-edge problems in NLP in order to provide the best-possible experience for our customers. As a Research Scientist, you will develop novel algorithms and modeling techniques to advance the state of the art, and simplify the path to apply these advances to measure the quality of Amazon’s latest devices and services product. You will build relationships with stakeholders and partner teams across multiple orgs, analyze data for trends, select suitable rule-based or machine-learning based techniques and advise team members, closing the loop through data, model, application and customer feedback. You will also leverage Amazon’s heterogeneous data sources and large-scale computing resources.You will work in a small science team with a fast-pace, self-driven environment. You will lead building the NLP algorithms and work with scientists who have deep domain knowledge in Computer Vision, Audio Processing, Signal Processing and Data Science. You team with a group of hardware and software engineers to build intelligent Robotics as well as large, scalable cloud services. You will get strong support from the engineering team during the stages of data capturing, model evaluation and model deployment, so that you can dedicate your time in algorithm research.
US, VA, Virtual Location - Virginia
Excited by using massive amounts of data to develop Machine Learning (ML) and Deep Learning (DL) models? Want to help public sector, medical center and non-profit agencies derive business value through the adoption of Artificial Intelligence (AI)? Eager to learn from many different enterprise’s use cases of AWS ML and DL? Thrilled to be key part of Amazon, who has been investing in Machine Learning for decades, pioneering and shaping the world’s AI technology?At Amazon Web Services (AWS), we are helping large enterprises build ML and DL models on the AWS Cloud. We are applying predictive technology to large volumes of data and against a wide spectrum of problems. Our Professional Services organization works together with our AWS customers to address their business needs using AI.AWS Professional Services is a unique consulting team. We pride ourselves on being customer obsessed and highly focused on the AI enablement of our customers. If you have experience with AI, including building ML or DL models, we’d like to have you join our team. You will get to work with an innovative company, with great teammates, and have a lot of fun helping our customers.If you do not live in a market where we have an open Data Scientist position, please feel free to apply. Our Data Scientists can live in any location where we have a Professional Service office.A successful candidate will be a person who enjoys diving deep into data, doing analysis, discovering root causes, and designing long-term solutions. It will be a person who likes to have fun, loves to learn, and wants to innovate in the world of AI. Major responsibilities include:· Understand the customer’s business need and guide them to a solution using our AWS AI Services, AWS AI Platforms, AWS AI Frameworks, and AWS AI EC2 Instances .· Assist customers by being able to deliver a ML / DL project from beginning to end, including understanding the business need, aggregating data, exploring data, building & validating predictive models, and deploying completed models to deliver business impact to the organization.· Use Deep Learning frameworks like PyTorch, Tensorflow and MxNet to help our customers build DL models.· Use SparkML and Amazon Machine Learning (AML) to help our customers build ML models.· Work with our Professional Services Big Data consultants to analyze, extract, normalize, and label relevant data.· Work with our Professional Services DevOps consultants to help our customers operationalize models after they are built.· Assist customers with identifying model drift and retraining models.· Research and implement novel ML and DL approaches, including using FPGA.· This position can have periods of up to 10% travel.· This position requires that the candidate selected be a US Citizen and obtain and maintain an active TS/SCI security clearance.· Here 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 we host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.
US, WA, Seattle
Our vision at Amazon Comprehend Medical is to simplify and advance Natural Language Processing (NLP) in Healthcare. We are building a team of passionate people who are focused on changing the world for the better. Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Automatic Speech Recognition (ASR), Machine Translation (MT), Natural Language Understanding (NLU), Imaging and Machine Learning (ML). If you want your work to positively impact the lives of millions of people and you’re up for a challenge, let’s talk!We are looking for a passionate, talented, and inventive Senior Applied Scientists who can bring bleeding edge machine learning and NLP techniques into real products solving real problems together with a highly multi-disciplinary team of scientist, engineers, strategic partners, product managers and subject domain experts.
US, FL, Virtual Location - Florida
The AWS Central Economics team is looking for a PhD economist. The ideal candidate will have experience with time-series forecasting.You will learn about cloud products, including compute, storage, and databases. You will work on analytic projects requested by senior leadership. You will get the opportunity to learn new techniques. You will be a part of a team with many experienced economists.
US, WA, Bellevue
Are you interested in applying your strong quantitative analysis and big data skills to world-changing problems? Are you interested in driving the development of methods, models and systems for state-of-the-art robotics, transportation and fulfillment systems? If so, then this is the job for you.The Amazon Transportation Services team is responsible for developing an in-depth understanding of our current network and designing our future networks. We are looking for a motivated and experienced Data Science Lead with outstanding leadership skills, proven ability to develop, automate, and manage analytical models of our systems. The successful candidate will have strong modeling skills and is comfortable owning their own data and working from concept through to execution. This role will also build tools and support structures needed to analyze data, dive deep into data to determine root cause of forecast/buying systems errors & changes, and present findings to business partners to drive improvements.A qualified candidate must have demonstrated ability to manage large-scale modeling projects, identify requirements and build methodology and tools that are statistically grounded. The ideal candidate will have experience collaborating across organizational boundaries, applying statistical methods to data, developing optimization and machine learning models.Want to learn more about working with Amazon Transportation Services? Check out this video! https://www.youtube.com/watch?v=en5YqrtBGvY&feature=youtu.be#NALH
US, CA, San Diego
The Economic Technology team (ET) is looking for a Senior Applied Scientist to join our team in building Reinforcement Learning solutions at scale. The ET applies Machine Learning, Reinforcement Learning, Causal Inference, and Econometrics/Economics to derive actionable insights about the complex economy of Amazon’s retail business. We also develop Statistical Models and Algorithms to drive strategic business decisions and improve operations. We are an interdisciplinary team of Economists, Engineers, and Scientists incubating and building day one solutions using cutting-edge technology, to solve some of the toughest business problems at Amazon.You will work with business leaders, scientists, and economists to translate business and functional requirements into concrete deliverables, including the design, development, testing, and deployment of highly scalable distributed services. You will partner with scientists, economists, and engineers to help invent and implement scalable ML, RL, and econometric models while building tools to help our customers gain and apply insights. This is a unique, high visibility opportunity for someone who wants to have business impact, dive deep into large-scale economic problems, enable measurable actions on the Consumer economy, and work closely with scientists and economists. We are particularly interested in candidates with experience building predictive models and working with distributed systems.As a Senior Applied Scientist, you bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions.