  This paper investigates the use of automatically collected web audio data for
the task of spoken language recognition. We generate semi-random search phrases
from language-specific Wikipedia data that are then used to retrieve videos
from YouTube for 107 languages. Speech activity detection and speaker
diarization are used to extract segments from the videos that contain speech.
Post-filtering is used to remove segments from the database that are likely not
in the given language, increasing the proportion of correctly labeled segments
to 98%, based on crowd-sourced verification. The size of the resulting training
set (VoxLingua107) is 6628 hours (62 hours per language on the average) and it
is accompanied by an evaluation set of 1609 verified utterances. We use the
data to build language recognition models for several spoken language
identification tasks. Experiments show that using the automatically retrieved
training data gives competitive results to using hand-labeled proprietary
datasets. The dataset is publicly available.
