Zobrazeno 1 - 10
of 66
pro vyhledávání: '"Vishwa Gupta"'
Autor:
Vishwa Gupta, Gilles Boulianne
Publikováno v:
Speech and Computer ISBN: 9783031209796
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5cc031918ff350e6ffcefdd2c5d0e679
https://doi.org/10.1007/978-3-031-20980-2_21
https://doi.org/10.1007/978-3-031-20980-2_21
Autor:
Nathan Thanyehténhas Brinklow, Alain Désilets, S. Z. Child, Benoit Farley, Eddie Antonio Santos, Akwiratékha' Martin, Anna Kazantseva, Fineen Davis, Christopher Cox, Delasie Torkornoo, Heather Souter, Brian Maracle Owennatékha, Eric Joanis, Gilles Boulianne, Olivia Sammons, Rebecca Knowles, Darlene A. Stewart, Marie-Odile Junker, Patrick Littell, Delaney Lothian, Vishwa Gupta, Caroline Running Wolf, Daisy Rosenblum, Roland Kuhn, David Huggins-Daines, Aidan Pine
Publikováno v:
COLING
This paper describes a three-year project at the National Research Council of Canada aimed at developing software to assist Indigenous communities in their efforts to preserve their languages and extend their use. The project aimed to work within the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9627dc3cdc6226aaef4b066aa19a4341
https://doi.org/10.18653/v1/2020.coling-main.516
https://doi.org/10.18653/v1/2020.coling-main.516
Publikováno v:
The Journal of the Association of Physicians of India. 68(1)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::686d397ccb8363a0d2f3b3d91c6c5542
https://pubmed.ncbi.nlm.nih.gov/31979870
https://pubmed.ncbi.nlm.nih.gov/31979870
Publikováno v:
INTERSPEECH
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2ab213502a0df65f9afe311cd3b7789a
https://doi.org/10.21437/interspeech.2019-1131
https://doi.org/10.21437/interspeech.2019-1131
Publikováno v:
IEEE/ACM Transactions on Audio, Speech, and Language Processing. 24:1106-1118
In this paper, we present our audio fingerprinting system that detects a transformed copy of an audio from a large collection of audios in a database. The audio fingerprints in this system encode the positions of salient regions of binary images deri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9f50ef25d1c00adf1b12dc4fba58c6d4
https://doi.org/10.1109/taslp.2016.2541303
https://doi.org/10.1109/taslp.2016.2541303
Publikováno v:
INTERSPEECH
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::63c81d602d48f062e142cea3374a57e8
https://doi.org/10.21437/interspeech.2018-1688
https://doi.org/10.21437/interspeech.2018-1688
Autor:
Vishwa Gupta, Gilles Boulianne
Publikováno v:
INTERSPEECH
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::62d548e48eedab1932c6f3bc5a0c08ee
https://doi.org/10.21437/interspeech.2018-2079
https://doi.org/10.21437/interspeech.2018-2079
Publikováno v:
Multimedia Tools and Applications. 75:9145-9165
This paper presents a novel audio fingerprinting method that is highly robust to a variety of audio distortions. It is based on an unconventional audio fingerprint generation scheme. The robustness is achieved by generating different versions of the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a241ebf95e551d98476a7fe4b0433744
https://doi.org/10.1007/s11042-015-3081-8
https://doi.org/10.1007/s11042-015-3081-8
Publikováno v:
ICASSP
This paper describes a video fingerprinting system that is highly robust to audio and video transformations. The proposed system adapts a robust audio fingerprint extraction approach to video fingerprinting. The audio fingerprinting system converts t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a8f5a7fd48a3369a9b510b02b82189ac
https://doi.org/10.1109/icassp.2017.7952715
https://doi.org/10.1109/icassp.2017.7952715
Modelling speaker and channel variability using deep neural networks for robust speaker verification
Publikováno v:
SLT
We propose to improve the performance of i-vector based speaker verification by processing the i-vectors with a deep neural network before they are fed to a cosine distance or probabilistic linear discriminant analysis (PLDA) classifier. To this end
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1ac0b76ad9db5e04d8fd87fd5a775a09
https://doi.org/10.1109/slt.2016.7846264
https://doi.org/10.1109/slt.2016.7846264