Multitask learning

Only 15% of submissions to “boutique” conference are accepted; four Amazon papers to be presented.

Training a product discovery system on many languages at once improves performance in all of them.

At next week’s Interspeech, the largest conference on the science and technology of spoken-language processing, Alexa researchers have 16 papers, which span the five core areas of Alexa functionality.

A text-to-speech system, which converts written text into synthesized speech, is what allows Alexa to respond verbally to requests or commands...

Alexa currently has more than 90,000 skills, or abilities contributed by third-party developers — the Uber ride-sharing skill, the Jeopardy! trivia game skill, the Starbucks drink-ordering skill, and so on.

Many of today’s most useful AI systems are multilabel classifiers: they map input data into multiple categories at once. An object recognizer, for instance, might classify a given image as containing sky, sea, and boats but not desert or clouds.

As Alexa expands into new countries, she usually has to be trained on new languages. But sometimes, she has to be re-trained on languages she’s already learned. British English, American English, and Indian English, for instance, are different enough that for each of them, we trained a new machine learning model from scratch.

The idea of using arrays of microphones to improve automatic speech recognition (ASR) is decades old. The acoustic signal generated by a sound source reaches multiple microphones with different time delays. This information can be used to create virtual directivity, emphasizing a sound arriving from a direction of interest and diminishing signals coming from other directions. In voice recognition, one of the more popular methods for doing this is known as “beamforming”.