Scaling Speech Technology to 1,000+ Languages

Expanding the language coverage of speech technology has the potential to improve access to information for many more people. However, current speech technology is restricted to about one hundred languages which is a small fraction of the over 7,000 languages spoken around the world. The Massively Multilingual Speech (MMS) project increases the number of supported languages by 10-40x, depending on the task. The main ingredients are a new dataset based on readings of publicly available religious texts and effectively leveraging self-supervised learning. We built pre-trained wav2vec 2.0 models covering 1,406 languages, a single multilingual automatic speech recognition model for 1,107 languages, speech synthesis models for the same number of languages, as well as a language identification model for 4,017 languages. Experiments show that our multilingual speech recognition model more than halves the word error rate of Whisper on 54 languages of the FLEURS benchmark while being trained on a small fraction of the labeled data

Synthesis of mesoporous Stober silica nanoparticles: the effect of secondary and tertiary alkanolamines

The effect of secondary (diethanolamine) and tertiary (triethanolamine) alkanolamines as catalysts on the formation of mesoporous Stober silica nanoparticles by sol-gel method was studied. The particles were characterized by thermogravimetry and differential thermal analysis, Fourier transform infrared spectroscopy, N-2 physisorption measurements, and field emission scanning electron microscopy. By using ammonia and different alkanolamines as catalysts, the Brunauer-Emmet-Teller (BET) surface area and pore volume increased in the order of ammonia diethanolamine > triethanolamine. The role of different alkanolamines on the textural properties and particle size of silica is explained in terms of their relative steric hindrance and basicity