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ZEINALI, H. ČERNOCKÝ, J. BURGET, L.
Original Title
A multi purpose and large scale speech corpus in Persian and English for speaker and speech Recognition: the DeepMine database
Type
conference paper
Language
English
Original Abstract
DeepMine is a speech database in Persian and English designedto build and evaluate text-dependent, text-prompted, and textindependentspeaker verification, as well as Persian speech recognitionsystems. It contains more than 1850 speakers and 540 thousandrecordings overall, more than 480 hours of speech are transcribed. Itis the first public large-scale speaker verification database in Persian,the largest public text-dependent and text-prompted speaker verificationdatabase in English, and the largest public evaluation dataset fortext-independent speaker verification. It has a good coverage of age,gender, and accents. We provide several evaluation protocols foreach part of the database to allow for research on different aspectsof speaker verification. We also provide the results of several experimentsthat can be considered as baselines: HMM-based i-vectorsfor text-dependent speaker verification, and HMM-based as well asstate-of-the-art deep neural network based ASR. We demonstratethat the database can serve for training robust ASR models.
Keywords
speech database, text-dependent, text-independent,speaker verification, speech recognition
Authors
ZEINALI, H.; ČERNOCKÝ, J.; BURGET, L.
Released
14. 12. 2019
Publisher
IEEE Signal Processing Society
Location
Sentosa, Singapore
ISBN
978-1-7281-0306-8
Book
IEEE Automatic Speech Recognition and Understanding Workshop - Proceedings (ASRU)
Pages from
397
Pages to
402
Pages count
6
URL
https://www.fit.vut.cz/research/publication/12153/
BibTex
@inproceedings{BUT161477, author="Hossein {Zeinali} and Jan {Černocký} and Lukáš {Burget}", title="A multi purpose and large scale speech corpus in Persian and English for speaker and speech Recognition: the DeepMine database", booktitle="IEEE Automatic Speech Recognition and Understanding Workshop - Proceedings (ASRU)", year="2019", pages="397--402", publisher="IEEE Signal Processing Society", address="Sentosa, Singapore", doi="10.1109/ASRU46091.2019.9003882", isbn="978-1-7281-0306-8", url="https://www.fit.vut.cz/research/publication/12153/" }
Documents
zeinali_asru2019_0000397.pdf