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Showing posts tagged with Alexa research

April 18, 2019

Ming Sun

Alexa scientists use semi-supervised learning and "pyramidal" neural networks to address the problems of sound identification and media detection.

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April 11, 2019

Jun Yang

Novel reconfigurable-filter-bank design enables more precise control of signal waveforms.

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April 08, 2019

Quynh Do

Transfer of a model co-trained on intent classification and slot (variable) tagging halved the data required to achieve a given level of performance.

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April 04, 2019

Hari Parthasarathi

To make it computationally feasible to train a speech recognizer on a million hours of speech, Alexa scientists used an array of techniques that could generalize to other large-scale machine learning projects.

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April 01, 2019

Kenichi Kumatani

Echo devices use information about sound sources' directions to isolate speech signals. Turning speech isolation and automatic speech recognition into a single, large, machine learning problem improves speech recognition accuracy, even on devices with fewer microphones.

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March 28, 2019

Yuan-yen Tai

Acoustic watermarking, which identifies audio signals through noise patterns imperceptible to humans, breaks down when signals are broadcast and re-captured by microphones. A new Alexa system is the first to solve this problem in real time, to prevent false device wakes and aid echo cancellation.

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March 21, 2019

Rahul Gupta

To produce synthetic training data for a machine-learning application where real data is scarce, Alexa scientist Rahul Gupta uses generative adversarial learning, which pits two neural nets against each other -- one trying to generate convincing fakes, the other trying to discern fake from real.

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March 20, 2019

Minhua Wu

Using one neural network to label speech data, adding synthetic environmental noise to that data, and then using it to train a second neural network improves speech recognition, particularly under noisy conditions.

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March 11, 2019

Ming Sun

Imbalances in the data used to train machine learning systems can cause biases; a new method for correcting imbalances increases the accuracy of a sound detection system by 22% over systems that used the previous method.

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March 05, 2019

Behnam Hedayatnia

As the Alexa Prize Socialbot Challenge 3 gears up, applied scientist Behnam Hedayatnia reviews a few of the innovations from the university teams who participated in the previous challenge.

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February 27, 2019

Anu Venkatesh

Tools include a contextual topic model, a sensitive-content detector, a conversation evaluator, and an integrated development environment. 

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January 31, 2019

Mike Rodehorst

"Acoustic fingerprinting" tells Alexa when specific instances of her name are safe to ignore.

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January 30, 2019

Alessandro Moschitti

A novel transfer-learning technique enables the addition of new classification categories to an existing machine learning model without access to the model's original training data.

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January 24, 2019

Alessandro Moschitti

New method for comparing data structures enables natural-language-understanding system to learn in four hours what used to take more than seven days.

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January 22, 2019

Anuj Goyal

A new approach leverages hundreds of millions of unannotated Alexa requests to improve the quality of "transfer learning", or adapting an existing neural network, trained on abundant data, to a new task for which training data is scarce.

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