Favorite All voices are unique, yet speakers tend to adjust their delivery, or speaking style, according to their context and audience. Before Amazon Polly used Neural Text-to-Speech technology (NTTS) to build voices, TTS (Standard Text-to-Speech) voices couldn’t change their speech patterns to match any particular speaking style. When Amazon Polly
Favorite We recently announced that Amazon Transcribe now supports transcription for audio and video for 7 additional languages including Gulf Arabic, Swiss German, Hebrew, Japanese, Malay, Telugu, and Turkish languages. Using Amazon Transcribe, customers can now take advantage of 31 supported languages for transcription use cases such as improving customer service, captioning and subtitling,
Favorite This is a guest post from Matt Fielder and Jordan Rosenblum at iHeartRadio. In their own words, “iHeartRadio is a streaming audio service that reaches tens of millions of users every month and registers many tens of thousands more every day.” Personalization is an important part of the user
Favorite To have an effective conversation, it is important to understand the sentiment and respond appropriately. In a customer service call, a simple acknowledgment when talking to an unhappy customer might be helpful, such as, “Sorry to hear you are having trouble.” Understanding sentiment is also useful in determining when
Favorite Amazon SageMaker Ground Truth helps you build highly accurate training datasets for machine learning. It can reduce your labeling costs by up to 70% using automatic labeling. This blog post explains the Amazon SageMaker Ground Truth chaining feature with a few examples and its potential in labeling your datasets.
Favorite This is a guest post from James Jameson, the Commercial Lead at CaptionHub. CaptionHub is a London-based company that focuses on video captioning and subtitling production for enterprise organizations. While the act of captioning—that is, taking video files and making sure the text on the screen reflects what’s being
Favorite If you’re like most companies, you wish to better understand your customers and your brand image. You’d like to track the success of your marketing campaigns, and the topics of interest—or frustration—for your customers. Social media promises to be a rich source of this kind of information, and many
Favorite Machine learning (ML) lets enterprises unlock the true potential of their data, automate decisions, and transform their business processes to deliver exponential value to their customers. To help you take advantage of ML, Amazon SageMaker provides the ability to build, train, and deploy ML models quickly. Until recently, if
Favorite AWS DeepRacer, launched at re:Invent 2018, helps developers get hands on with reinforcement learning (RL). Since then, thousands of people have developed and raced their models at 21 AWS DeepRacer League events at AWS Summits across the world, and virtually via the AWS DeepRacer console. Beyond the summits there
Favorite Machine learning (ML) is routinely used by countless businesses to assist with decision making. In most cases, however, the predictions and business decisions made by ML systems still require the intuition of human users to make judgment calls. In this post, I show how to combine ML with sensitivity
Shared by AWS Machine Learning November 25, 2019
