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.
The demand for Generative AI in Media and Entertainment Market size is expected to register USD 1,412.7 million by 2023. It is anticipated to showcase a steady CAGR of 26.3% between 2023 and 2032. Sales of generative AI in media and entertainment will likely register USD 11,570.0 million by 2032. Revenue stood at USD 1,158.5 million in 2022.
This comprehensive financial analyst course covers a wide range of financial subjects, including financial statement analysis, ratio analysis, cash flow analysis, valuation methods, bookkeeping, and VAT. Participants will also develop expertise in payroll management, functioning as an accounts assistant, and mastering basic to advanced Excel techniques.
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.
R. O'Donnell. (2021)cite arxiv:2105.10386Comment: First edition originally published April 2014, in hardcover book format by Cambridge University Press, and electronically on the author's website. This arXiv version corrects 100+ typos and errors, but is otherwise essentially the same.
F. Haak. Information between Data and Knowledge, volume 74 of Schriften zur Informationswissenschaft, Werner Hülsbusch, Glückstadt, Gerhard Lustig Award Papers.(2021)
A. Park, B. Beck, D. Fletche, P. Lam, and H. Tsang. 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), page 880-883. (August 2016)
P. D, C. Veeramani, B. Shalini, and R. Karthika. International Journal of Innovative Science and Modern Engineering (IJISME), 2 (10):
41-45(September 2014)
A. Grigoryan, and S. Agaian. Applied Mathematics and Sciences: An International Journal (MathSJ), volume 1 of IFIP Advances in Information and Communication Technology, page 23-39. Springer, (December 2014)
G. Singh. (2012)Fraud Detection is of great importance to financial institutions. In this paper we have tried to study the Outlier Analysis in Stock Market Fraud Detection. Outlier Analysis is a fundamental issue in Data Mining, specifically in Fraud Detection. While observing the Indian Stock Market, we could detect that some of the Trading Entities have suspicious trading patterns that give rise to a doubt of having some malpractices in stock transactions within Indian Stock Market. All the facts are presented on the basis of data obtained from the official sites of BSE (Bombay Stock Exchange), NSE (National Stock Exchange) and SEBI (Securities and Exchange Board of India)..
G. Singh. (2013)By comparing historical data of trading like daily Open, High, Low, Close, Volume, Number of Trades, Turnover, Delivery percentage etc. of a particular stock with its Peer Group companies and Non Peer Group companies stocks for a particular period, we can find some unusual observations which are also known as outliers. In this paper we have tried to detect the observations, which are very different from the other observations using a Data Mining Technique for Outlier Detection-“Multiple Linear Regression Analysis”..
H. Mubarak, S. Chowdhury, and F. Alam. (2022)cite arxiv:2203.00271Comment: Gender Analysis Dataset, Demography, Arabic Twitter Accounts, Arabic Social Media Content.