Amazon Developer Blogs

Amazon Developer Blogs

Showing posts tagged with Alexa Research

September 17, 2019

Dilek Hakkani-Tur

Data set includes more than 230,000 dialogue turns, most of which are annotated to indicate the sources of their factual assertions.

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September 16, 2019

Shuyang Gao

Treating a conversation as a text, and dialogue state tracking as answering questions about the text, enables an 11.75% improvement in accuracy over the best-performing prior system.

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September 10, 2019

Larry Hardesty

Research spans the five core areas of Alexa functionality, as well as more-general questions in machine learning.

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September 05, 2019

Pranav Ladkat

By combining two state-of-the-art techniques for parallelizing machine learning — one that prioritizes accuracy, one that prioritizes efficiency — Alexa researchers improve on both.

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September 04, 2019

Dilek Hakkani-Tur

Universal dialogue-act tagging scheme, hybrid slot-tracking system promise to improve dialogue state tracking.

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August 29, 2019

Anirudh Raju

Techniques include weighting training samples from out-of-domain data sets and noise contrastive estimation, which turns the calculation of massive probability distributions into simple binary decisions.

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August 22, 2019

Jaime Lorenzo Trueba

Two Interspeech papers report a system that transfers prosody — inflection and rhythm — from a recorded speaker to a synthesized voice and a neural vocoder that works with any speaker.

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August 15, 2019

Boya Yu

Based on embeddings, system suggests named entities — or "slot values" — that developers might want their skills to recognize.

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August 13, 2019

Chieh-Chi Kao

Two new papers explore techniques for increasing the computational efficiency and reducing the memory footprints of neural networks that process audio data.

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August 08, 2019

Mengwen Liu

Pooling the training data for related skills, and using it to train the skills simultaneously, improves performance for all of them.

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