8/13/2023 0 Comments Ibm speech to text spanish![]() They ran experiments using 10 languages, from Romance languages like Italian and Spanish to languages that have completely different alphabets, like Russian and Mandarin. For French and Spanish, they have 97 percent overlap,” Lai says. But the surprising part is that if we prune these models, they will end up with highly similar pruning patterns. “We would expect these two models to be very different because they are finetuned for different languages. Lai and his collaborators wanted to see how the pruning process would affect this model’s speech recognition performance.Īfter pruning the full neural network to create a smaller subnetwork, they trained the subnetwork with a small amount of labeled Spanish speech and then again with French speech, a process called finetuning. Just like a gardener cuts off superfluous branches, neural network pruning involves removing connections that aren’t necessary for a specific task, in this case, learning a language. The researchers set out to improve the efficiency of this network by pruning it. However, the neural network has about 300 million individual connections, so it requires a massive amount of computing power to train on a specific language. This opens the door for speech recognition of uncommon languages that lack large amounts of transcribed speech, like Wolof, which is spoken by 5 million people in West Africa. The training process only requires a few minutes of transcribed speech. Wave2vec 2.0 is a self-supervised learning model, so it learns to recognize a spoken language after it is fed a large amount of unlabeled speech. The researchers studied a powerful neural network that has been pretrained to learn basic speech from raw audio, called Wave2vec 2.0.Ī neural network is a series of algorithms that can learn to recognize patterns in data modeled loosely off the human brain, neural networks are arranged into layers of interconnected nodes that process data inputs. The research will be presented at the Conference on Neural Information Processing Systems in December. Liu, Yi-Lun Liao, Sameer Khurana, and Yung-Sung Chuang his advisor and senior author James Glass, senior research scientist and head of the Spoken Language Systems Group in CSAIL MIT-IBM Watson AI Lab research scientists Yang Zhang, Shiyu Chang, and Kaizhi Qian and David Cox, the IBM director of the MIT-IBM Watson AI Lab. Lai wrote the paper with fellow MIT PhD students Alexander H. “This is an important problem to solve because we have amazing technology in natural language processing and speech recognition, but taking the research in this direction will help us scale the technology to many more underexplored languages in the world,” says Cheng-I Jeff Lai, a PhD student in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and first author of the paper. This technology could even be used to transcribe and document rare languages that are in danger of vanishing. Automatic speech-recognition can also help users learn new languages and improve their pronunciation skills. The systems are important in some academic environments, where they can assist students who are blind or have low vision, and are also being used to improve efficiency in health care settings through medical transcription and in the legal field through court reporting. This work could help level the playing field and bring automatic speech-recognition systems to many areas of the world where they have yet to be deployed. ![]() Because only small tweaks are needed once the larger model is cut down to size, it is much less expensive and time-consuming to teach this model an uncommon language. Their technique involves removing unnecessary parts of a common, but complex, speech recognition model and then making minor adjustments so it can recognize a specific language. Researchers at MIT and elsewhere have now tackled this problem by developing a simple technique that reduces the complexity of an advanced speech-learning model, enabling it to run more efficiently and achieve higher performance. However, these solutions are often too complex and expensive to be applied widely. ![]() Recent advances have enabled machine learning models that can learn the world’s uncommon languages, which lack the large amount of transcribed speech needed to train algorithms. ![]() Automated speech-recognition technology has become more common with the popularity of virtual assistants like Siri, but many of these systems only perform well with the most widely spoken of the world’s roughly 7,000 languages.īecause these systems largely don’t exist for less common languages, the millions of people who speak them are cut off from many technologies that rely on speech, from smart home devices to assistive technologies and translation services. ![]()
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