Posts in category: Amazon AWS

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.Read More

The first step in training a neural network to solve a problem is usually the selection of an architecture: a specification of the number of computational nodes in the network and the connections between them. Architectural decisions are generally based on historical precedent, intuition, and plenty of trial and error.Read More

Today I am happy to announce the public release of the Topical Chat Dataset, a text-based collection of more than 235,000 utterances (over 4,700,000 words) that will help support high-quality, repeatable research in the field of dialogue systems.Read More

During a conversation between a customer and a dialogue system like Alexa’s, the system must not only understand what the customer is saying currently but also remember the conversation history. Only by combining the history with the current utterance can the system truly understand the customer’s requirements.Read More

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.Read More

Earlier this year, we reported a speech recognition system trained on a million hours of data, a feat possible through semi-supervised learning, in which training data is annotated by machines rather than by people. These sorts of massive machine learning projects are becoming more common, and they require distributing the training process across multiple processors. Otherwise, training becomes too time consuming.Read More

Earlier this year, at Amazon’s re:MARS conference, Alexa head scientist Rohit Prasad unveiled Alexa Conversations, a new service that allows Alexa skill developers to more easily integrate conversational elements into their skills. The announcement is an indicator of the next stage in Alexa’s evolution: more-natural, dialogue-based engagements that enable Alexa to aggregate data and refine requests to better meet customer needs.Read More

An automatic-speech-recognition system — such as Alexa’s — converts speech into text, and one of its key components is its language model. Given a sequence of words, the language model computes the probability that any given word is the next one. For instance, a language model would predict that a sentence that begins “Toni Morrison won the Nobel” is more likely to conclude “Prize” than “dries”. Language models can thus help decide between competing interpretations of the same acoustic information.Read More

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

Embedding entity names from diverse skills in a shared representations space enables system to suggest neglected entity names with 88.5% accuracy.Read More