Unspeech: Unsupervised Speech Context Embeddings
Table 1: Comparison of English speech data sets.
['[BOLD] dataset', '[BOLD] hours  [BOLD] train', '[BOLD] hours  [BOLD] dev', '[BOLD] hours  [BOLD] test', '[BOLD] speakers  [BOLD] train', '[BOLD] speakers  [BOLD] dev', '[BOLD] speakers  [BOLD] test']
TED-LIUM and Common Voice In Common Voice, volunteers from all over the world recorded predefined prompts, in TED-LIUM utterances are segmented from and aligned to TED talks. In order to explore large-scale training, we also downloaded all TEDx talks from 01-01-2016 until 03-01-2018 from YouTube, giving us 41520 talks (0.5 TB compressed audio data) with a total of 9505 hours of unannotated audio. While the majority of TEDx talks are in English, a very small number of them are in other languages or contain only music. We did not segment or clean the TEDx data.