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ZEINALI, H. ČERNOCKÝ, J. BURGET, L.
Originální název
A multi purpose and large scale speech corpus in Persian and English for speaker and speech Recognition: the DeepMine database
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
DeepMine is a speech database in Persian and English designed to build and evaluate text-dependent, text-prompted, and textindependent speaker verification, as well as Persian speech recognition systems. It contains more than 1850 speakers and 540 thousand recordings overall, more than 480 hours of speech are transcribed. It is the first public large-scale speaker verification database in Persian, the largest public text-dependent and text-prompted speaker verification database in English, and the largest public evaluation dataset for text-independent speaker verification. It has a good coverage of age, gender, and accents. We provide several evaluation protocols for each part of the database to allow for research on different aspects of speaker verification. We also provide the results of several experiments that can be considered as baselines: HMM-based i-vectors for text-dependent speaker verification, and HMM-based as well as state-of-the-art deep neural network based ASR. We demonstrate that the database can serve for training robust ASR models.
Klíčová slova
speech database, text-dependent, text-independent, speaker verification, speech recognition
Autoři
ZEINALI, H.; ČERNOCKÝ, J.; BURGET, L.
Vydáno
14. 12. 2019
Nakladatel
IEEE Signal Processing Society
Místo
Sentosa, Singapore
ISBN
978-1-7281-0306-8
Kniha
IEEE Automatic Speech Recognition and Understanding Workshop - Proceedings (ASRU)
Strany od
397
Strany do
402
Strany počet
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/" }