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In June 2018, Amazon announced the 11 focus areas of the 2018 Amazon Research Awards.

We describe an intelligent context-aware conversational system that incorporates screen context information to service multimodal user requests. Screen content is used for disambiguation of utterances that refer to screen objects and for enabling the user to act upon screen objects using voice commands. We propose a deep learning architecture that jointly models the user utterance and the screen and incorporates

We propose a framework that harnesses visual cues in an unsupervised manner to learn the co-occurrence distribution of items in real-world images for complementary recommendation. Our model learns a non-linear transformation between the two manifolds of source and target item categories (e.g., tops and bottoms in outfits). Given a large dataset of images containing instances of co-occurring items, we train

The Alexa Auto SDK contains essential client-side software required to integrate Alexa into the automobile. The Auto SDK provides libraries that connect to Alexa and expose interfaces for your vehicle software to implement the platform-specific behavior for audio input, media streaming, calling through a connected phone, turn-by-turn navigation, controlling vehicle features such as heaters and lights, and

Language models are a key component of automatic speech recognition systems, which convert speech into text. A language model captures the statistical likelihood of any particular string of words, so it can help decide between different interpretations of the same sequence of sounds.

This morning, I am delivering a keynote talk at the World Wide Web Conference in Lyon, France, with the title, Conversational AI for Interacting with the Digital and Physical World.

Here’s a fairly common interaction with Alexa: “Alexa, set volume to five”; “Alexa, play music”. Even though the queries come in quick succession, the customer needs to repeat the wake word “Alexa”. To allow for more natural interactions, the device could immediately re-enter its listening state after the first query, without wake-word repetition; but that would require it to detect whether a follow-up speech input is indeed a query intended for the device (“device-directed”) or just background speech (“non-device-directed”).

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.

KTH has a long history of research on conversational systems. Our team of graduate and undergraduate students has a wide range of expertise in linguistics, cognitive science, machine learning and artificial intelligence.

Just as Alexa can wake up without the need to press a button, she also automatically detects when a user finishes her query and expects a response. This task is often called “end-of-utterance detection,” “end-of-query detection,” “end-of-turn detection,” or simply “end-pointing.”

This year’s Interspeech — the largest conference in speech technology — will take place in Hyderabad, India, the first week of September. More than 40 Amazon researchers will be attending, including Björn Hoffmeister, the senior manager for machine learning in the Alexa Automatic Speech Recognition group. He took a few minutes to answer three questions about this year’s conference.

Multimedia streaming services over spoken dialog systems have become ubiquitous. User-entity affinity modeling is critical for the system to understand and disambiguate user intents and personalize user experiences. However, fully voice-based interaction demands quantification of novel behavioral cues to determine user affinities. In this work, we propose using play duration cues to learn a matrix factorization

We present a personalized recommender system using neural network for recommending products, such as eBooks, audio-books, Mobile Apps, Video and Music. It produces recommendations based on customer’s implicit feedback history such as purchases, listens or watches. Our key contribution is to formulate recommendation problem as a model that encodes historical behavior to predict the future behavior using

Extraction of missing attribute values is to find values describing an attribute of interest from a free text input. Most past related work on extraction of missing attribute values work with a closed world assumption with the possible set of values known beforehand, or use dictionaries of values and hand-crafted features. How can we discover new attribute values that we have never seen before? Can we do

We tackle the problem of optimizing a blackbox objective function defined over a highly structured input space. This problem is ubiquitous in machine learning. Inferring the structure of a neural network or the Automatic Statistician (AS), where the kernel combination for a Gaussian process is optimized, are two of many possible examples. We use the AS as a case study to describe our approach, that can

Fast expansion of natural language functionality of intelligent virtual agents is critical for achieving engaging and informative interactions. However, developing accurate models for new natural language domains is a time and data intensive process. We propose efficient deep neural network architectures that maximally re-use available resources through transfer learning. Our methods are applied for expanding

Alexa’s ability to act on spoken requests depends on statistical models that translate speech to text and text to actions. Historically, the models’ decisions were one-size-fits-all: the same utterance would produce the same action, regardless of context.

Last week, Amazon announced the release of both a redesigned Echo Show with a bigger screen and the Alexa Presentation Language, which enables third-party developers to build “multimodal” skills that coordinate Alexa’s natural-language-understanding systems with on-screen graphics.

This paper introduces a brand new tree library treelite. The library is a toolbox to facilitate easy deployment of models and accelerate prediction performance. It has a Python wrapper that allows users to integrate it as part of their workflow. Treelite is able to read tree ensemble models that are trained by any tree libraries, including XGBoost [1], LightGBM [2], and scikit-learn [3]. Treelite is also

This paper proposes an efficient real-time multirate fast transient-sound detection algorithm on the basis of emerging microphone array configuration intended for multimedia signal processing application systems such as digital smart home. The proposed detection algorithm first extracts the dynamics and periodicity features, then trains the model parameters of these features on Amazon machine learning platform