AI Technology & Industry Review
In the new paper Scaling Speech Technology to 1,000+ Languages, a Meta AI research team launches the company’s Massively Multilingual Speech (MMS) project, which aims to dramatically expand access to speech technologies.
In the new paper Mu2SLAM: Multitask, Multilingual Speech and Language Models, a Google Research team presents Mu2SLAM, a multilingual sequence-to-sequence pretraining method for speech and text models that covers arbitrary tasks in over 100 languages.
A research team from Carnegie Mellon University and Google systematically explores strategies for leveraging the relatively under-studied resource of bilingual lexicons to adapt pretrained multilingual models to low-resource languages. Their resulting Lexicon-based Adaptation approach produces consistent performance improvements without requiring additional monolingual text.
An Apple research team explores multiple architectures and training procedures to develop a novel multi-speaker and multi-lingual neural TTS system. The study combines speech from 30 speakers from 15 locales in 8 languages, and demonstrates that for the vast majority of voices, such multi-lingual and multi-speaker models can yield better quality than single speaker models.
A Google Research team proposes MergeDistill, a framework for merging pretrained teacher LMs from multiple monolingual/multilingual LMs into a single multilingual task-agnostic student LM to leverage the capabilities of the powerful language-specific LMs while still being multilingual and enabling positive language transfer.
An IBM research team proposes four multilingual adversarial attack strategies and attacks seven languages in a zero-shot setting on large multilingual pretrained language models (e.g. MBERT), reducing average performance by up to 85.6 percent.