The Video Annotation for Speech Technologies (VAST) corpus contains approximately 2900 hours of video data collected and labeled to support the development of speech technologies such as speech activity detection, language identification, speaker identification, and speech recognition. The bulk of the data comes from amateur video content harvested from the web. Collection was designed to ensure that the videos cover a diverse range of communication domains, data sources and video resolutions and to include three primary languages (English, Mandarin Chinese and Arabic) plus supplemental data in 7 additional languages/dialects to support language recognition research. Portions of the collected data were annotated for speech activity, speaker identity, speaker sex, language identification, diarization, and transcription. A description of the data collection and each of the annotation types is presented in this paper. The corpus represents a challenging data set for language technology development due to the informal nature of the majority of the data, as well as the variety of languages, noise conditions, topics, and speakers present in the collection.
@InProceedings{TRACEY18.1015, author = {Jennifer Tracey and Stephanie Strassel}, title = "{VAST: A Corpus of Video Annotation for Speech Technologies}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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