Posts in category: Amazon AWS

Automatic speech recognition systems, which convert spoken words into text, are an important component of conversational agents such as Alexa. These systems generally comprise an acoustic model, a pronunciation model, and a statistical language model. The role of the statistical language model is to assign a probability to the next word in a sentence, given the previous ones. For instance, the phrases “Pulitzer Prize” and “pullet surprise” may have very similar acoustic profiles, but statistically, one is far more likely to conclude a question that begins “Alexa, what playwright just won a … ?”Read More

To be as useful as possible to customers, Alexa should be able to make educated guesses about the meanings of ambiguous utterances. If, for instance, a customer says, “Alexa, play the song ‘Hello’”, Alexa should be able to infer from the customer’s listening history whether the song requested is the one by Adele or the one by Lionel Richie.Read More

This month, Amazon announced the 11 focus areas of the 2018 Amazon Research Awards.Read More

Amazon Alexa currently has more than 40,000 third-party skills, which customers use to get information, perform tasks, play games, and more. To make it easier for customers to find and engage with skills, we are moving toward skill invocation that doesn’t require mentioning a skill by name (as highlighted in a recent post).Read More

Alexa is a cloud-based service with natural-language-understanding capabilities that powers devices like Amazon Echo, Echo Show, Echo Plus, Echo Spot, Echo Dot, and more. Alexa-like voice services traditionally have supported small numbers of well-separated domains, such as calendar or weather. In an effort to extend the capabilities of Alexa, Amazon in 2015 released the Alexa Skills Kit, so third-party developers could add to Alexa’s voice-driven capabilities. We refer to new third-party capabilities as skills, and Alexa currently has more than 40,000.Read More

Developing a new Alexa skill typically means training a machine-learning system with annotated data, and the skill’s ability to “understand” natural-language requests is limited by the expressivity of the semantic representation used to do the annotation. So far, the techniques used to represent natural language have been fairly simple, so Alexa has been able to handle only relatively simple requests.Read More

As Alexa-enabled devices continue to expand into new countries, we propose an approach for quickly bootstrapping machine-learning models in new languages, with the aim of more efficiently bringing Alexa to new customers around the world.Read More

Amazon scientists are continuously expanding Alexa’s natural-language-understanding (NLU) capabilities to make Alexa smarter, more useful, and more engaging.Read More

Smart speakers, such as the Amazon Echo family of products, are growing in popularity among consumer and business audiences. In order to improve the automatic speech recognition (ASR) and full-duplex voice communication (FDVC) performance of these smart speakers, acoustical echo cancellation (AEC) and noise reduction systems are required. These systems reduce the noises and echoes that can impact operation, such as an Echo device accurately hearing the wake word “Alexa.”Read More

In recent years, the amount of textual information produced daily has increased exponentially. This information explosion has been accelerated by the ease with which data can be shared across the web. Most of the textual information is generated as free-form text, and only a small fraction is available in structured format (Wikidata, Freebase etc.) that can be processed and analyzed directly by machines.Read More