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MIT Announces 'Natural' Object ID Model Using Audio Descriptions
The Massachusetts Institute of Technology (MIT) announced this week that the university's computer scientists have invented a new machine learning model for object recognition that incorporates audio descriptions (versus transcripts of audio) along with images.
"The model doesn't require manual transcriptions and annotations of the example [speech] it's trained on," the official announcement explained of the new method. "Instead, it learns words directly from recorded speech clips and objects in raw images, and associates them with one another."
Typically, most machine learning models that incorporate audio require transcriptions of that audio versus using the audio itself. While this current system only recognizes "several hundred words and object types," the researchers who developed it have high hopes for its future.
"We wanted to do speech recognition in a way that's more natural, leveraging additional signals and information that humans have the benefit of using, but that machine learning algorithms don't typically have access to," commented David Harwath, a researcher in MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Spoken Language Systems Group.
"There's potential there for a Babel Fish-type of mechanism," he continued.
This specific experiment is built upon a 2016 project, but with more images and data added, and with a new approach to the training. Specific details on how the model was trained can be found in the official announcement of the new project here.
About the Author
Becky Nagel serves as vice president of AI for 1105 Media specializing in developing media, events and training for companies around AI and generative AI technology. She also regularly writes and reports on AI news, and is the founding editor of PureAI.com. She's the author of "ChatGPT Prompt 101 Guide for Business Users" and other popular AI resources with a real-world business perspective. She regularly speaks, writes and develops content around AI, generative AI and other business tech. She has a background in Web technology and B2B enterprise technology journalism.