Join Technovation Girls and learn how to use technology like mobile apps and AI to solve a community problem YOU care about. You'll work as part of a team of girls like you and get support from a mentor who will help keep you motivated and on track.
aiEDU is a non-profit that creates equitable learning experiences that build foundational AI literacy. Whether you have nine weeks or just five minutes, we have an engaging, free curriculum that’s easy to use.
Today, speech technology is only available for a small fraction of the thousands of languages spoken around the world because traditional systems need to be trained on large amounts of annotated speech audio with transcriptions. Obtaining that kind of data for every human language and dialect is almost impossible.
Wav2vec works around this limitation by requiring little to no transcribed data. The model uses self-supervision to push the boundaries by learning from unlabeled training data. This enables speech recognition systems for many more languages and dialects, such as Kyrgyz and Swahili, which don’t have a lot of transcribed speech audio. Self-supervision is the key to leveraging unannotated data and building better systems.
J. Houssart, and H. Evens. Proceedings of the sixth British Congress of Mathematics Education held at the University of Warwick, page 65-72. bsrlm, (2005)
H. Evens, and J. Houssart. Educational Research, 46 (3):
269-282(2004)This paper utilizes Toulmin's original framework to analyse over 400 answers given by 11-year-olds to a question on a written mathematics test. The question required children to say whether a given statement is true and give a written explanation. Categorizations of answers are developed from the data and examined, suggesting that many children appeared to understand the mathematics but were not able to give adequate explanations. Findings are also compared with other researchers' findings. In contrast to other studies, a large category of non-valid answers appear mathematical, but are largely restatement of the information the children were given. Although only a minority provided explanations deemed worthy of a mark, further analysis demonstrates greater degrees of comprehension than this suggests. Teaching strategies for building children's expressive and specifying skills are identified..
B. Mott, S. McQuiggan, S. Lee, S. Lee, and J. Lester. Proceedings of the Agent Based Systems for Human Learning Workshop at the 5th International Joint Conference on Autonomous Agents and Multiagent Systems (ABSHL-2006), Hakodate, Japan, (2006)
M. Cerulli, A. Chioccariello, and E. Lemut. Proceedings of the Fourth Congress of the European Society for Research in Mathematics Education (CERME 4), page 591-600. Sant Feliu de Guíxols, Spain,, (2005)
S. Alexander, and J. Hedberg. Proceedings of the IFIP TC3/WG3.2 Working Conference on the Seign, Implementation and Evaluation of Interactive Multimedia in University Settings, page 233--244. New York, NY, USA, Elsevier Science Inc., (1994)