AI News, Machine-learning system tackles speech and object recognition, all at once

Machine-learning system tackles speech and object recognition, all at once

Unlike current speech-recognition technologies, the model doesn't require manual transcriptions and annotations of the examples it's trained on.

But the researchers hope that one day their combined speech-object recognition technique could save countless hours of manual labor and open new doors in speech and image recognition.

'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.

In the paper, the researchers demonstrate their model on an image of a young girl with blonde hair and blue eyes, wearing a blue dress, with a white lighthouse with a red roof in the background.

The model learned to associate which pixels in the image corresponded with the words 'girl,' 'blonde hair,' 'blue eyes,' 'blue dress,' 'white light house,' and 'red roof.'

If the model learns speech signals from language A that correspond to objects in the image, and learns the signals in language B that correspond to those same objects, it could assume those two signals -- and matching words -- are translations of one another.

After comparing thousands of wrong captions with image A, the model learns the speech signals corresponding with image A, and associates those signals with words in the captions.

As described in a 2016 study, the model learned, for instance, to pick out the signal corresponding to the word 'water,' and to retrieve images with bodies of water.

With the correct image and caption pair, the model matches the first cell of the grid to the first segment of audio, then matches that same cell with the second segment of audio, and so on, all the way through each grid cell and across all time segments.

'The biggest contribution of the paper,' Harwath says, 'is demonstrating that these cross-modal [audio and visual] alignments can be inferred automatically by simply teaching the network which images and captions belong together and which pairs don't.'

Machine-learning system tackles speech and object recognition, all at once

MIT computer scientists have developed a system that learns to identify objects within an image, based on a spoken description of the image.

“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.

In the paper, the researchers demonstrate their model on an image of a young girl with blonde hair and blue eyes, wearing a blue dress, with a white lighthouse with a red roof in the background.

The model learned to associate which pixels in the image corresponded with the words “girl,” “blonde hair,” “blue eyes,” “blue dress,” “white light house,” and “red roof.” When an audio caption was narrated, the model then highlighted each of those objects in the image as they were described.

If the model learns speech signals from language A that correspond to objects in the image, and learns the signals in language B that correspond to those same objects, it could assume those two signals — and matching words — are translations of one another.

With the correct image and caption pair, the model matches the first cell of the grid to the first segment of audio, then matches that same cell with the second segment of audio, and so on, all the way through each grid cell and across all time segments.

“The biggest contribution of the paper,” Harwath says, “is demonstrating that these cross-modal [audio and visual] alignments can be inferred automatically by simply teaching the network which images and captions belong together and which pairs don’t.” The authors dub this automatic-learning association between a spoken caption’s waveform with the image pixels a “matchmap.” After training on thousands of image-caption pairs, the network narrows down those alignments to specific words representing specific objects in that matchmap.

“Predictions start dispersed everywhere but, as you go through training, they converge into an alignment that represents meaningful semantic groundings between spoken words and visual objects.” “It is exciting to see that neural methods are now also able to associate image elements with audio segments, without requiring text as an intermediary,” says Florian Metze, an associate research professor at the Language Technologies Institute at Carnegie Mellon University.

Machine-learning system tackles speech and object recognition

MIT computer scientists have built up a system that figures out how to distinguish protests inside an image, in view of a spoken description of the picture. Given an image and a sound inscription, the model will feature continuously the important regions of the image being described.

David Harwath, a researcher in the Computer Science and Artificial Intelligence Laboratory said, “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.

The model learned to associate which pixels in the image corresponded with the words ‘girl’, ‘blonde hair’, ‘blue eyes’, ‘blue dress’, ‘white lighthouse’, and ‘red roof’.

By recognizing correct image and caption pair, the model matches the first cell of the grid to the first segment of audio, then matches that same cell with the second segment of audio. For each cell and audio segment, it provides a similarity score, depending on how closely the signal corresponds to the object.

Dr. Harwath said, “The challenge is that, during training, the model doesn’t have access to any true alignment information between the speech and the image. The biggest contribution of the paper is demonstrating that these cross-modal alignments can be inferred automatically by simply teaching the network which images and captions belong together and which pairs don’t.”

Harwath said, “It’s kind of like the Big Bang, where the matter was really dispersed, but then coalesced into planets and stars. Predictions start dispersed everywhere but, as you go through training, they converge into an alignment that represents meaningful semantic groundings between spoken words and visual objects.”

MIT researchers combine voice and object recognition in new model

Researchers at MIT have developed a new system that combines voice and object recognition capable of identifying an object within an image given only a spoken description of that image.

“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,” David Harwath, a researcher in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Spoken Language Systems Group, said.

“We got the idea of training a model in a manner similar to walking a child through the world and narrating what you’re seeing.” Right now the model can only recognize several hundred different words and object types, but researchers hope that one day this model will be able to open new doors in speech and image recognition and reduce the time spent on manual labor.

MIT boffins develop system that can recognise speech and objects at the same time

MIT computer scientists have developed a system that tackles speech and object recognition at the same time.

In their new paper, the researchers demonstrate their model on an image of a young girl with blonde hair and blue eyes, wearing a blue dress, with a white lighthouse with a red roof in the background.

The model learned to associate which pixels in the image corresponded with the words 'girl,' 'blonde hair,' 'blue eyes,' 'blue dress,' 'white lighthouse,' and 'red roof.'

If the model learns speech signals from language A that correspond to objects in the image and learns the signals in language B that correspond to those same objects, it could assume those two signals - and matching words - are translations of one another.'

He and his fellow MIT researchers also hope that one day their combined speech-object recognition technique could save countless hours of manual labour and open new doors in speech and image recognition.

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