Evaluating speech synthesis in many languages with SQuId

Posted by Thibault Sellam, Research Scientist, Google Previously, we presented the 1,000 languages initiative and the Universal Speech Model with the goal of making speech and language technologies available to billions of users around the world. Part of this...

Scaling audio-visual learning without labels

Researchers from MIT, the MIT-IBM Watson AI Lab, IBM Research, and elsewhere have developed a new technique for analyzing unlabeled audio and visual data that could improve the performance of machine-learning models used in applications like speech recognition and...