Alexa

Self-learning system uses customers’ rephrased requests as implicit error signals.

According to listener tests, whispers produced by a new machine learning model sound as natural as vocoded human whispers.

Analysis points to better algorithms for product discovery.

Related data selection techniques yield benefits for both speech recognition and natural-language understanding.

Today is the fifth anniversary of the launch of the Amazon Echo, so in a talk I gave yesterday at the Web Summit in Lisbon, I looked at how far Alexa has come and where we’re heading next.

In a paper we’re presenting at this year’s Conference on Empirical Methods in Natural Language Processing, we describe experiments with a new data selection technique.

This year at the Conference on Empirical Methods in Natural-Language Processing (EMNLP), we will cohost the Second Workshop on Fact Extraction and Verification — or FEVER — which will explore techniques for automatically assessing the veracity of factual assertions online.

In the past few weeks, Amazon announced versions of Alexa in three new languages: Hindi, U.S. Spanish, and Brazilian Portuguese. Like all new-language launches, these addressed the problem of how to bootstrap the machine learning models that interpret customer requests, without the ability to learn from customer interactions.

Amazon today announced the public release of a new data set that will help speech scientists address the difficult problem of separating speech signals in reverberant rooms with multiple speakers. In the field of automatic speech recognition, this problem is known as the “cocktail party” or “dinner party” problem; accordingly, we call our data set the Dinner Party Corpus, or DiPCo.