Toward speech recognition for uncommon spoken languages

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. Because 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. 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. However, these solutions are often too complex and expensive to be applied widely. 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.