To accomplish this ambitious goal, we need to address two significant challenges in ASR.įirst, there is a lack of scalability with conventional supervised learning approaches. Moreover, our model training process is effective at adapting to new languages and data.Ī sample of the languages that USM supports. In “ Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages”, we demonstrate that utilizing a large unlabeled multilingual dataset to pre-train the encoder of the model and fine-tuning on a smaller set of labeled data enables us to recognize under-represented languages. USM, which is for use in YouTube (e.g., for closed captions), can perform automatic speech recognition (ASR) not only on widely-spoken languages like English and Mandarin, but also on under-resourced languages like Amharic, Cebuano, Assamese, and Azerbaijani to name a few. USM is a family of state-of-the-art speech models with 2B parameters trained on 12 million hours of speech and 28 billion sentences of text, spanning 300+ languages. Today, we are excited to share more about the Universal Speech Model (USM), a critical first step towards supporting 1,000 languages. However, some of these languages are spoken by fewer than twenty million people, so a core challenge is how to support languages for which there are relatively few speakers or limited available data. Last November, we announced the 1,000 Languages Initiative, an ambitious commitment to build a machine learning (ML) model that would support the world’s one thousand most-spoken languages, bringing greater inclusion to billions of people around the globe. Posted by Yu Zhang, Research Scientist, and James Qin, Software Engineer, Google Research

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