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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.
L. Lee. Approaches to algebra: perspectives for research and teaching, Kluwer Academic Publishers, p 102
… it is much of a challenge to demonstrate that functions, modelling, and problem solving are all types of generalizing activities, that algebra and indeed all of mathematics is about generalizing patterns.
p 103
The history of the science of algebra is the story of the growth of a technique for representing of finite patterns.
The notion of the importance of pattern is as old as civilization. Every art is founded on the study of patterns.
Mathematics is the most powerful technique for the understanding of pattern, and for the analysis of the relationships of patterns.(1996)
M. Cerulli, A. Chioccariello, and E. Lemut. 5th CERME conference - congress of European Society for Research in Mathematics Education, Larnaca, Cyprus, (2007)
D. Carraher, A. Schliemann, and B. Brizuela. Proceedings of the XXV Conference of the International Group for the Psychology of Mathematics Education, (2001)