Citation analysis was traditionally based on data from the ISI Citation indexes. Now with the appearance of Scopus, and with the free citation tool Google Scholar methods and measures are need for comparing these tools. In this paper we propose a set of measures for computing the similarity between rankings induced by ordering the retrieved publications in decreasing order of the number of citations as reported by the specific tools. The applicability of these measures is demonstrated and the results show high similarities between the rankings of the ISI Web of Science and Scopus and lower similarities between Google Scholar and the other tools.
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.