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.
Deutscher Wortschatz contains data generated from newspapers and web resources that are publicly available. The data were collected per language and encompass statistics about co-occurrences of words in randomly selected sentences.
Web 0.1: Wetter, Fuball-Live-Ticker, Fernsehprogramm: 16 Millionen Deutsche drcken jeden Tag auf die Fernbedienung, um an Informationen des Videotexts zu kommen. (tags: tv text)